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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.

Wetlands play an important role for water, carbon storage and biodiversity in the seasonally dry and hot environments of tropical savannas. Peatlands, which are permanent wetlands, are important as the carbon-richest parts of wetland ecosystems with a strong ability to store carbon, retain water and regulate its flow. With this first review on peatlands in the Cerrado we synthesize existing knowledge and gaps on their distribution and types in the biome including, vegetation, soil properties, carbon stocks and hydrogeomorphology. Peatlands are embedded in wetland complexes in valleys, groundwater-fed oligo- to mesotrophic, with wet grass- and shrubland, Vereda or riparian swamp forest vegetation. Average peat depth is 1.4 meters and soil carbon stocks in the first meter can be 9 times higher than in mineral soils under Cerrado dryland vegetation, reaching about 1000 t carbon per hectare. Total soil carbon stock estimates (3.19 Gt C) in peatlands equal 13.3% of the total soil carbon in the Cerrado in only 0.7% of its total area, although large uncertainties exist. Actual peatland occurrence appears to be more abundant than current soil and peat maps suggest. The high rate of transformation of the native vegetation into industrial agriculture and wood plantations, which affects large parts of the Cerrado, is a major cause for the degradation and the loss of peatlands and other wetlands. However, the extent of peatland degradation and resulting carbon losses remain unfathomed. We identified research needs such as better mapping and monitoring, and recommend including peatlands into wetland classification systems in Brazil.

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.

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 (CaCO3) equivalent is determined via manocalcimetry. For calcareous (carbonate bearing) samples, SIC is estimated as 12 % of CaCO3 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 TRDCNGS. 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 TRDCGS. 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 TRDCGS variant as best for calcareous samples, whereas the TRDCNGS 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.

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.

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.

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.

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 (R2 = 0.80, RMSE = 0.74 %) and Local (R2 = 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.

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 R2 = 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.

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 (R2 = 0.83, RPD = 2.42). While workflow two and the traditional PLSR approach was more robust for all input dates (R2 = 0.79, RPD = 2.21). Best results of the single images reached R2 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.

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.

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 (

Sustainable cropland management requires quantitative and up-to-date information on the spatial pattern of soil organic carbon (SOC) at scales relevant for implementing targeted conservation measures. Spectra-based remote sensing of SOC in croplands is promising, but it requires the extraction of high-quality bare soil pixels that enable spatially continuous coverage. Here, we aim to compare the SOC predictive capability of single-date versus multitemporal compositing of bare soil images in an intensively cultivated region (4,700 km2) of northeast China. A series of 12 bare soil images within 2017–2022 were processed and passed onto three multitemporal compositing approaches (geometric median, and univariate mean and median) to create bare soil mosaics. Both single-date and multitemporal spectral images, together with laboratory-simulated Sentinel-2 benchmark data, were used to develop partial least squares regression, Cubist and random forest models via 100 bootstrapped validations. With Cubist consistently being the best performing algorithm for all three data sources, results show that the 12 single-date images exhibited temporally unstable performance (R2: 0.30–0.67). Among the three compositing approaches, the high-dimensional geometric median composite was the most suitable because of (i) its close resemblance to laboratory reference and robustness to outliers, which yielded a Cubist model (R2: 0.64; RMSE: 2.24 g/kg) outperforming 11 out of 12 single-date models; and (ii) its ability to retain between-band spectral relationships that allowed further incorporation of SOC-relevant bare soil indexes, which led to a further 6.5 % increase in model prediction accuracy. The resultant SOC map highlighted the capability of Sentinel-2 bare soil imaging to reveal field-scale soil degradation patterns. Future work should explore the possibility of extending the purely spectra-based framework to integrated SOC mapping and monitoring with additional soil biophysical and management information.

Soil organic carbon (SOC) is central to the functioning of terrestrial ecosystems, has climate mitigation potential and provides several benefits for soil health. Understanding the spatial distribution of SOC can help formulate sustainable soil management practices. Digital soil mapping (DSM) uses advanced statistical and geostatistical methods to estimate soil properties across large areas. DSM integrates climate data, topographic features, geology, legacy soil maps, land management and remote sensing data. Bare soil spectra may reflect the presence of particular soil components, making satellite derived spectra suitable predictors of SOC. Bare soil spectra derived from Sentinel-2 were used to estimate SOC concentration (SOC%) and granulometric fractions in the plough layer (0–30 cm) of agricultural parcels in northern Belgium. Thereafter, the estimation performance of SOC% was compared for three DSM models: one with bare soil spectra, one with environmental covariates (topography, granulometry and vegetation), and a combined model with bare soil spectra and environmental covariates. The estimation performance of sand, silt and clay fractions using bare soil spectra from the spring seedbed (R2: 0.53–0.74; RPD: 1.49–2.05; RPIQ: 1.52–2.39) was higher than that of SOC% (R2: 0.16; RPD: 1.08; RPIQ: 1.32). The highest estimation performance of SOC% was obtained for a DSM model including all covariates (R2: 0.28; RPD: 1.18; RPIQ: 1.44), but the contribution of spring seedbed spectra to a model containing environmental covariates was small. The results provide valuable insights for refining soil property estimation using DSM with spectral and environmental covariates.

This study examines the commitments of African countries that would support improved soil security and soil health. Measures for agroforestry, erosion control, grassland management, soil organic carbon (SOC), and wetlands were assessed within the frameworks of the Rio Conventions. By analyzing national commitments in Land Degradation Neutrality (LDN) targets, and biodiversity (Aichi) targets, and Nationally Determined Contributions (NDCs), submitted until January 31, 2024, the research identifies the extent and quantification of these commitments. Results reveal a varying emphasis on these measures across different national targets, with significant focus on agroforestry, grassland, SOC, and wetland conservation in LDN targets and NDCs. Where quantified commitments for a single measure have been set across LDN, NDC, and biodiversity targets, the necessity of national coordination in setting, planning, and monitoring sustainable soil management commitments is highlighted. The study concludes that implementing sustainable soil management practices remain pivotal for achieving agricultural and environmental goals in Africa, thereby calling for a unified approach to leverage existing commitments towards setting future priorities in soil health and soil condition, drawing attention to the critical need for integrated national processes to ensure effective implementation and monitoring of these commitments.

Agricultural soil contamination is increasing day-by-day and becoming a major problem over the globe. Trace elements accumulation in the bulk soil is frequently documented, however, there is no precise mechanism of their distribution in the different soil aggregates level. We collected twelve composite soil samples from banana fields, fallow land, rice cultivated with pond water (rice field-I), and rice cultivated with rain water (rice field-II). We separated soil samples into four different size of aggregates (4-2, 2–0.25, 0.25–0.053, 0.05). Moreover, macroaggregate acted as an accumulator for Fe, Mn, and As, while all the aggregate fractions acted as accumulators for Cu and Zn. Our study indicated that MWD, SOC, aggregate size and composition, and metal species were the controlling factors of trace elements accumulation and distribution in the various sizes of soil aggregates.

Soil organic carbon (SOC) is a critical component that affects soil quality and global carbon cycling. Current SOC mapping approaches are based on the spatial stationarity relationship of SOC and soil formation processes. Nevertheless, the spatial pattern of SOC is the consequence of different soil-forming factors and processes that operate at different scales. In this work, we hypothesized that the covariation of environmental variables and SOC might differ spatially, and proposed a global (whole area) and local analysis framework that aimed to enhance our comprehension of the explanatory scale of environmental variables on SOC variation. This framework primarily incorporates Geographically Weighted correlation and the Multi-scale Geographically Weighted Regression (MGWR) model. With 216 farmland topsoil samples collected from the Qilu Lake watershed in Yunnan Province, China (area of 354 km2), we explored both global and local relationships between environmental variables and SOC to verify the feasibility of this framework. Results showed that the explanatory power of environmental variables on SOC variation is scale-dependent. Our analysis revealed that certain variables, which may explain local variations of SOC, are often overlooked due to their insignificant global correlation with SOC (p > 0.05). For example, in this case study, soil porosity and two landscape metrics that characterize the anthropogenic processes of land use patterns can effectively explain the local spatial variation of SOC. They improved the model performance of MGWR, but their global correlation with SOC is not significant. The proposed framework highlights the necessity of investigating the explanatory power of environmental variables on a global and local scale.

Thinning affects the understorey microclimate and the material input into the shelter forest soil, and it alters the contribution of the plant-derived and microbial-derived carbon (C). This difference effects the utilization of C by soil microbes and ultimately soil C emission. There is still no adequate characterization for the contribution of plant-derived and microbial-derived C to soil organic C (SOC) under thinning. Moreover, how this difference affects microbially driven C emissions (soil microbial respiration, SMR) and the contribution to the soil priming effect in this process are still unknown. The contribution of plant-derived and microbial-derived C to SOC were quantified by comparing lignin phenols and amino sugars under thinning and control treatments. Meanwhile, a 30-day indoor cultivation experiment was conducted. Soil priming effect and SMR were tested by adding 13C-labelled glucose to the soil of the thinning and control treatments. The purpose was to investigate the contribution of C from different sources to SOC, as well as its influence mechanism on soil priming effect and SMR.The results indicate that thinning significantly increased the fungal-derived (10.19 and 7.34 g kg−1) and microbial-derived C (11.05 and 8.19 g kg−1) of Quercus acutissima Carruth (QAC) and Pinus thunbergii Parl (PTP). However, the bacterial-derived C (0.86 and 0.85 g kg−1) decreased. Compared to the control treatment, the plant-derived, fungal-derived and microbial-derived C increased by 6.6 % and 1.1 %, 17.9 % and 12.2 %, and 16.6 % and 10.1 %, respectively, while bacterial-derived C decreased by 1.3 % and 2.1 %. Thinning reduced the soil priming effect of QAC by lowering the pH and increasing F/B and fungal-derived C. At the same time, this phenomenon was not evident in PTP, mainly attributed to the content of SOC and vanillyl and syringyl monomers. Thinning reduced the SMR, which was mainly related to the suppression of soil priming effect. In summary, this study has demonstrated that thinning reduced the soil priming effect and SMR of shelter forests by altering the contribution of plant-derived and microbial-derived C to SOC.

An improved understanding of preferential solute transport in soil macropores would enable more reliable predictions of the fate of agrochemicals and the protection of water quality in agricultural landscapes. The objective of this study was to investigate how soil organic carbon (SOC) and soil texture shape soil pore structure and thereby determine the susceptibility to preferential transport under steady-state near-saturated flow conditions. To do so, we took intact topsoil samples from an arable field that has large variations in SOC content (1.1–2.7%) and clay content (8–42%). Soil pore structure was quantified by X-ray tomography and soil water retention measurements. Non-reactive solute transport experiments under steady-state near-saturated conditions were carried out at irrigation rates of 2 and 5 mm h−1 to quantify the degree of preferential transport. Near-saturated hydraulic conductivities at pressure heads of −1.3 and −6 cm were also measured using a tension disc infiltrometer. The results showed that larger abundances of small macropores (240–720 µm diameter) and mesopores (5–100 µm diameter) resulted in weaker preferential transport, due to larger hydraulic conductivities in the soil matrix that prevented the activation of water flow and solute transport in large macropores. In particular, the degree of preferential transport was most strongly and negatively correlated with the mesoporosity in the 30–100 µm diameter class. In contrast, the degree of preferential transport was not correlated with connectivity measures (e.g. the percolating fraction and critical pore diameter for the macropore network), probably because i.) the pore space of almost all samples was highly connected, being dominated by one percolating cluster, and ii.) only a part of this percolating macroporosity was active under the near-saturated conditions of the experiment. We also found that the degree of preferential transport was strongly and negatively correlated with clay content, whilst the effects of SOC were not significant. Nevertheless, macroporosity in the 240–720 µm diameter class and mesoporosity were positively correlated with SOC content in our soils and in some previous studies. Therefore, SOC sequestration in arable soils may potentially reduce the risk of preferential transport under near-saturated flow conditions through better developed networks of small macropores and mesopores.

Many global environments face increasing pressures on soil resources, and effective, scalable methods for assessment of soil condition and capital are essential to respond to tangible soil threats. This situation is common across Pacific Island Countries and Territories (PICTs), where high throughput soil analysis laboratories are limited, and issues such as soil organic carbon decline, acidification and fertility declines are present. Soil spectral inference presents an opportunity in such regions to provide rapid insights into soil capital and condition, though the need for robust calibration libraries remains a limiting factor. This work investigates the utility of a regionally appropriate spectral library, the New Zealand Soil Spectral Library (NZSSL) to support the development of soil spectral inference in data-poor environments, such as PICTs, through a case study on the island of Tongatapu in The Kingdom of Tonga. We contrast the performance of existing partial least squares regression (PLSR) models developed for New Zealand soils on soils from Tongatapu and explore the opportunities for enhancement of predictions formed through memory-based learning (MBL) supplemented with local data. Our work shows the potential for cost-effective and timely soil monitoring through soil spectral inference in PICTs. The work further underscores the importance of regional cooperation and data-sharing for addressing soil security.

Accurately accounting for soil organic carbon stocks is crucial in assessing the sequestration potential of arid and semi-arid rangelands. This study, conducted in the rangelands near Loving, southeastern New Mexico, aims to evaluate soil organic carbon stocks, assess the potential for soil organic carbon sequestration, and identify both biotic and abiotic factors influencing this process. To achieve these goals, soil samples were collected and subjected to analysis for soil particle size, bulk density, soil characteristic curve, as well as soil total carbon and inorganic carbon. Soil total carbon stock (STCS), soil organic carbon stock (SOCS), soil inorganic carbon stock (SICS), and soil total nitrogen stock (STNS) were determined. Soil depth, vegetation types, and coverages on each ranch were identified. Additionally, soil ion concentrations, soil cation exchange capacity (CEC), and soil sodium adsorption ratio (SAR) were assessed. The soil CEC ranged from 6 to 20 meq/100 g soil, and SAR from 0.02 to 0.58 cmol/kg^0.5. The concentrations of Na and Cl were 42 and 13 mg/l, at a depth of 0–20 cm, increasing to 74 and 34 mg/l at 20–32 cm depth, respectively. In October 2021, STCS measured 39 Mg/ha at 0–20 cm and 11 Mg/ha at 20–32 cm soil depth. SOCS ranged from 18 Mg/ha at 0–20 cm to 6 Mg/ha at 20–32 cm soil depth, while SICS ranged from 20 to 1 Mg/ha at 0–20 cm and 20–32 cm soil depth, respectively. STNS varied from 1 Mg/ha at 0–20 cm to 3.8 Mg/ha at 20–32 cm soil depth. In the 20 cm sandy loam soil depth, ranches 1, 7, 8, 9, and 11 exhibited the highest SOCS, increased with increasing clay content, particularly where honey mesquite was the dominant species. These findings emphasize the importance of exploring soil carbon storage fluctuations in southern New Mexico. The differences in SOCS among ranches under different vegetation underscore the considerable potential for storing soil organic carbon in the semi-arid rangelands of this region.

Sustainable crop production requires nutrient inputs to replace what was removed or lost. Replenishing nutrients using fertilizers and animal wastes is one of the most common input practices. While there is considerable research evaluating the efficacy of fertilizer and manure-based inputs for crop performance, nutrient use efficiency, and the subsequent impact to the environment, the influence of these inputs on soil health properties is less understood. The goals of this review are to summarize the state of literature on the influence of chemical fertilizer and manure-based inputs on soil health and to identify research gaps to refine efforts toward improved soil health, sustainability, and soil carbon storage. Conclusions suggest that chemical fertilizer inputs can increase soil organic matter and soil health provided that additions are not beyond what a plant can utilize. Manure-based inputs contribute to soil organic carbon and associated metrics; however, direct comparison of products is difficult as they are generally applied on a nutrient basis rather than carbon basis, which differentially affects their influence on soil health. Overall, nutrient-containing inputs show beneficial influences on soil health metrics; however, more focused research into their influence on soil health is needed.

Plastic film mulching has been widely used to increase maize yield in the semiarid area of China. However, whether long-term plastic film mulching is conducive to agricultural sustainability in this region remains controversial. A field experiment was initiated in 2013 with five different film mulching methods: (i) control method, flat planting without mulching (CK), (ii) flat planting with half film mulching (P), (iii) film mulching on ridges and planting in narrow furrows (S), (iv) full film mulching on double ridges (D), and (v) film mulching on ridges and planting in wide furrows (R). The effects on soil organic carbon (SOC) content, storage, and fractions, and on the carbon management index (CMI) were evaluated after nine consecutive years of plastic film mulching. The results showed that long-term plastic film mulching generally maintained the initial SOC level. Compared with no mulching, plastic film mulching increased the average crop yield, biomass yield, and root biomass by 48.38, 35.06, and 37.32%, respectively, which led to the improvement of SOC sequestration. Specifically, plastic film mulching significantly improved CMI, and increased the SOC content by 13.59%, SOC storage by 7.47% and easily oxidizable organic carbon (EOC) by 13.78% on average, but it reduced the other labile fractions. SOC sequestration and CMI were improved by refining the plastic film mulching methods. The S treatment had the best effect among the four mulching methods, so it can be used as a reasonable film mulching method for sustainable agricultural development in the semiarid area.

Understanding the mechanisms that control soil organic carbon (SOC) persistence is central to soil management and climate change mitigation. In the present study, we utilised a chronosequence of Vertisols which have undergone land use change from native vegetation to cropping for up to 82 y in subtropical Australia. We examined whether the marked changes in SOC concentrations were associated with changes in the physicochemical protection of SOC in aggregate structures (occlusion) and mineral surfaces (adsorption) or with changes in chemical functional composition. Soil samples were fractionated using density and physical fractionation to isolate the free particulate organic matter (fPOM), occluded POM (oPOM) and fine mineral-associated organic matter (fine-MAOM) to assess the impact of land use change on soil organic matter (SOM) fractions with differing degrees of physicochemical protection. The impact of long-term cropping on SOC functional group composition across soil fractions was assessed using synchrotron-based near edge X-ray absorption fine structure (NEXAFS) analyses. We found that although long-term cropping caused a loss of 43 % of bulk SOC after 20 y, this marked loss over time was not associated with a change in C functional group composition. Furthermore, although the SOC retention in the various fractions differed up to 60-fold (fPOM-C decreased by 78 % after cropping for 20 y, whilst fine-MAOM decreased by 25 %), there were only comparatively minor differences in SOC functional group composition between these fractions. Together, these findings suggest that the differences in C retention between fractions were less related to SOC functional group composition and more related to SOM’s physicochemical protection.

Current cropping systems, like cover cropping, aim to improve soil health and crop productivity, with the former a more sustainable route to the latter. This can be done by evaluating the influence of cover crops (CCs) on soil health indicators, both abiotic and biotic, as is the objective of this study. Several CCs (crimson clover [Trifolium incarnatum L.], oats [Avena sativa], hairy vetch [Vicia villosa Roth.], winter wheat [Triticum aestivum L.], winter peas [Lathyrus hirsutus L.], flax [Linum usitassimum L.], triticale [Triticale hexaploide Lart.], cereal rye [Secale cereale], and barley [Hordeum vulgare L.]) were used across two research sites, set up using a completely randomized design with two levels of CCs (CC vs no cover crop [NC]) and three replicates during 2023. Soil samples were collected at 0–10, 10–20, and 20–30 cm depths and analyzed for soil physico-chemical properties and microbial biomass and composition. Results showed significantly lower bulk density values, greater water content (at 0 kPa soil water matric potential), greater volume-specific heat capacity (at 0 and -33 kPa soil water matric potential), greater total nitrogen, and numerically greater soil organic carbon under CC compared with NC management. This led to numerically greater microbial biomass and community composition (e.g., arbuscular mycorrhizal fungi, gram-negative and gram-positive bacteria, eukaryotes, and fungi), and slightly lower microbial stress indicators (genotypic and chemical structure categorizations) under CC compared with NC management. However, the lack of significant differences between treatments suggests that three years is insufficient to detect improvements in measured soil health indicators. Further, the significant differences in measured soil health indicators between study sites suggests an influence of soil texture and order, and this warrants further investigations.

Post-fire salvage logging is widely applied in Mongolia's boreal forests with the intent to prevent intact forests from logging. The rationale behind this approach is the assumption that the additional disturbance caused by the removal of standing deadwood after stand-replacing fire is of no further significance for the already heavily disturbed ecosystem. However, while there is a global debate on effects of salvage logging for regeneration success, biodiversity, and soil health, little evidence has been collected from strongly drought-limited southern boreal forests of Central Asia. Comparing sites with and without salvage logging, we investigated forests of Siberian larch (Larix sibirica) ca. 20 years after stand-replacing fire and asked whether postfire salvage logging affected regeneration density, terminal shoot length and radial stem increment, ecosystem carbon stock densities, and reduced organic layer depth and compacted the soil. The biomass of the larch regeneration was significantly reduced by salvage logging, while tree growth was not affected. The ecosystem carbon stock density of burnt forest without salvage logging was 202 Mg C ha−1 and thus even in the lower range of intact larch forests from Mongolia, whereas burnt forests with salvage logging had organic carbon stock densities (104 Mg C ha−1) that were lower than those of unburned grasslands in the forest-steppe. These results show that removing deadwood from burnt forest is not insignificant, but has the potential to delay forest recovery and strongly reduces organic carbon storage. However, we did not find significant reductions in soil organic carbon stocks or soil compaction. Nonetheless, our findings raise the question of whether careful management of intact forests (especially by selective felling under a continuous-cover forestry regime) would be a more ecologically sustainable alternative than post-fire salvage logging.

In geosciences, kriging is leading spatial interpolation, and co-kriging is the most commonly used method for accomplishing spatial interpolation of a target variable by incorporating information from a secondary variable. Co-kriging relies on the assumption of spatial stationarity, which may not hold true in all geospatial contexts, leading to potential inaccuracies in interpolation. The effectiveness of co-kriging can be compromised in areas with sparse data, impacting the reliability of interpolated results. Moreover, it can be resource-intensive when used for interpolation with a substantial volume of data, especially in the case of 3D interpolation. In this paper, we introduce a new method for spatial interpolation that considers two variables using a generative deep neural network. This approach utilizes a convolutional neural network with an encoder–decoder architecture, featuring a single encoder and two decoders to handle the two variables. Additionally, we introduce a loss function that facilitates the control over the relationships between the two variables. Traditional Deep Learning methods require prior training and labeled data, whereas the proposed approach eliminates this requirement and simplifies the interpolation process. In order to assess the performance of our method, we use two real-world datasets. The first one is a 2D dataset of total soil organic carbon combined with the Normalized Difference Vegetation Index. The second one is a 3D dataset that combines concentrations of Hydrocarbon and Fluoride obtained from hyperspectral analysis of soil cores with very limited number of boreholes. The experimental results demonstrate that the proposed method outperforms ordinary kriging and co-kriging, showing a significant improvement when both variables are used. We also demonstrate how the inclusion of the auxiliary variable serves as a means to mitigate the overfitting of the model.

Agricultural land use and temperature change can profoundly affect soil organic carbon (SOC), but their combined effects on regulating cropland SOC fractions over broad geographic scales remain poorly understood. Based on 871 topsoil (0–20 cm) samples collected from the Sichuan Basin of China, we used random forest model and partial correlations analysis to explore how the functionally distinct SOC fractions, including particulate and mineral-associated organic C (POC and MAOC), are affected by both agricultural land use and temperature over a large scale. The results show that POC accounts for 32.54 % of the total cropland SOC and soils with high SOC contents have larger proportions of POC. Cropland type is the dominant control of the two SOC fractions and regulates the impacts of other factors. The contents of soil POC and MAOC in dryland are much lower than that in paddy field and dryland-paddy rotation land. Significant correlations between mean annual temperature and the two SOC fractions are only found in dryland and dryland-paddy rotation land, while mean annual temperature has no impact on both POC and MAOC in paddy field. Meanwhile, the correlation coefficients between mean annual temperature and POC and MAOC for dryland decreased by 61.90 % and 69.23 %, respectively, and for dryland-paddy rotation land decreased by 28.00 % and 14.29 %, respectively, after removing correlations with net primary productivity. Additionally, correlation between mean annual temperature and POC is slightly higher than that between mean annual temperature and MAOC in dryland, while the reverse in dryland-paddy rotation land. These results suggest that the temperature effects on POC and MAOC are cropland type-dependent. To accurately predicting future cropland SOC change and reliably evaluate the potential contribution of cropland soil C sequestration, fraction-specific control mechanisms related to cropland type should be sufficiently considered in carbon models.

Release and transport of organic carbon (OC), a large portion of which is complexed with mineral colloids, can lead to long-term C sequestration. However, the role of mobile colloids in carbon cycling is not well understood. In this study, we conducted experiments using syringe columns to measure size-dependent colloid mobility and associated OC loadings under stepwise reductions of redox potential, representing existence of different electron acceptors. Colloids were extracted from both leachate and soil samples, designated as leachate colloids and soil colloids, respectively, and fractionated into different sizes: particulates (450–1000 nm), fine colloids (100–450 nm) and nano colloids (2–100 nm) using sequential centrifugation and ultrafiltration techniques. Increases in leachate solution pH and ionic strength (IS) with decreasing redox potential promoted colloid release, as indicated in the increase in turbidity values, due to increased electrostatic repulsion between colloids and soil matrix. The average OC loading by colloids, i.e., the amount of OC associated with per unit mass of colloids, in leachate colloids was up to four times that of soil colloids for the same size fraction. In addition, fine colloids had ∼2 times higher OC loading (∼0.4 mg C mg−1 colloids) than particulates (∼0.2 mg C mg−1 colloid) for leachate colloids. Moreover, measurements of C:N ratio and the natural abundance of stable δ13C and δ15N isotope signatures of soil colloids showed that the fine-colloid fraction experienced more significant fluctuations in these measurements as redox potential decreased than the other two fractions (i.e., particulates and nano colloids). The results of this study demonstrated that OC loading, composition and reactivity all varied with size and strongly influenced by redox conditions. This knowledge can help improve the understanding of how different phases of OC, e.g., dissolved vs. colloidal OC, may impact C sequestration and transport in soils.

Estimations of the patterns and controls of soil organic carbon (SOC) could provide instructive insights into the potential impact of future global change on soil carbon (C). In this work, we combined GeoDetector and random forest (RF) to estimate SOC stocks in Chinese grassland ecosystems with uncertainty assessments, and identified a network of cross-correlated environmental covariates for determining SOC based on a dataset collected from 813 sampling sites (0–20 cm) collected from 2000 to 2014. We predicted that 17.50 Pg C was stored in Chinese grasslands to a depth of 20 cm and that the average SOC density was 4.69 kg C/m−2(−|−). The effectiveness of using RF to predict SOC was demonstrated by an accuracy assessment based on 10-fold cross-validation, with a ratio of performance to deviation (RPD) of 2.89. The SOC stocks in southern China were lower than those in northern China. A high SOC density was found in northeastern China and on the Qinghai–Tibet Plateau. Soil properties had the strongest direct effect on SOC. Climate was significantly negatively associated with SOC and indirectly affected SOC via its effect on soil properties. Topography had a significant direct impact on vegetation, but its direct effect on SOC was relatively weak. This study emphasizes the patterns and heterogeneity of SOC stocks as well as the relative significance of climate, vegetation, soil characteristics, topography, and their complex interrelationships in controlling SOC. These results may provide a theoretical foundation for developing sustainable management systems and calibrating C process models.

The deteriorating state of soil fertility and low agricultural productivity in Ethiopia can be traced to the lack of equivalent consideration given to the soil's biological, chemical, and physical properties. A pot experiment was conducted to investigate the effect of mixed manure and blended nitrogen, phosphorus, sulfur and boron (NPSB) fertilizer on phosphorus adsorption, and other properties of Vertisols, nutrient uptake, and growth performance of maize. The study findings indicate that the combined application of mixed manure and blended NPSB significantly reduced soil pH from 7.87 to 7.68, phosphorus adsorption efficiency from 93 to 88.5 %, and Freundlich adsorption capacity from 194 to 100.75 mg kg−1 , intensity from 1.96 to 1.27 compared to control. However, combined application of these two treatments significantly increased the organic carbon from 0.81 to 1.64 %, total nitrogen from 0.04 to 0.13 %, and available phosphorus from 6.96 to 73.82 g kg−1. The study further revealed that mixed manure and blended NPSB resulted in significantly (p ≤ 0.05) higher contents of nitrogen and phosphorus in the maize leaves as well as their uptake compared to their sole application and control. The highest values of these parameters were observed in plots treated with a combined application of 15 t ha−1 mixed manure with each rate of 100 and 150 kg ha−1 blended NPSB. Additionally, the maize plant height (p ≤ 0.05) and above-ground biomass (p ≤ 0.01) also exhibited significant increase. Compared to the control and full dose of NPSB, all the treatments that received a combined application of 15 t ha−1 mixed manure with blended NPSB ranging from 50 to 150 kg ha−1 resulted in significantly higher above-ground biomass of maize. The results suggest that the combined use of mixed manure and blended NPSB could be a practical and effective approach to improve soil properties and maize above-ground biomass yield.

Soil organic carbon is critical to reducing anthropogenic CO2 emissions, highlighting the importance of carbon sequestration on agricultural lands. No-till agricultural practices have been shown to be effective in improving soil quality and sequestering carbon, thereby supporting global efforts to mitigate climate change. Based on the edaphoclimatic conditions of the region, five reference areas were selected, with different characteristics in terms of conditions (A1, A2, A3, A4, and A5) in 5 different farms in the municipality of Rio Verde, GO, Brazil. Soil and biomass samples left by the crop after harvest were collected. These areas represent 3 priority management systems in the Cerrado region of central Brazil, namely: no-till with soybean and maize in sequence in a rainfed system; no-till with soybean and maize in sequence in a central pivot irrigation system; first cut sugar cane; second cut sugar cane.In October 2022, 15 points per area were sampled for soil carbon stock estimation and for chemical and physical characterization purposes along linear transects. Transect sampling approach is widely used in soil attributes studies for comparison and data collection. Biomass samples were used to estimate soil biomass accumulation and carbon stock indices. The physic-chemical parameters of the soil in each area were determined through a complete analysis of the 0–20 cm profile. By presenting a new dataset of soil organic carbon stocks and carbon credits from several long-term no-till areas in central Brazil, this article contributes to the knowledge of the potential of conservation management systems for carbon trading, providing insights into their current contributions and future modelling.

Alluvial plains are vexing landscapes for soil mapping and spatial soil property predictions. Alluvial sediments often exhibit unpredictable spatial variability from both fluvial and anthropogenic disturbance. The determination of optimal number of soil sampling points for capturing soil variability remains a persistent issue for mapping and monitoring soil conditions. Here, soil organic matter (SOM) and cation exchange capacity (CEC) were estimated in an alluvial plain from a dense array of 2145 soil samples over 250 ha. The primary goals were to i) use pixel-based statistics from time-series Sentinel-2 satellite reflectance data to estimate SOM and CEC; ii) evaluate the use of images with vegetation cover versus bare soil images; and, iii) investigate the optimal number of sampling points to map alluvial soils using different sampling strategies. The optimal sample density was 1 sample per 2.5 ha based on the overlapping of prediction distributions (OV > 0.9). Conditioned Latin Hypercube Sampling (cLHS) was the most efficient sampling strategy. Random grid sampling provided the least consistent results. The use of cLHS coupled with the mean and standard deviation bands was the optimal sampling strategy. Pixel-based statistics from readily available satellite imagery captured persistent soil-reflectance relationships that enabled the use of row crops as proxies to predict soil properties. The combined use of pixel-based statistics as inputs to cLHS can reduce the chances of oversampling and associated costs. Pixel-based statistics provided persistent spatial information that can enhance precision agriculture management, carbon mapping, and is likely applicable to other soil properties and remotely sensed products. Use of pixel-based statistics from Sentinel-2 and similar global-coverage imagery could provide readily-calculable inputs for improved soil mapping and more optimal site sample selections in other agricultural row crop areas, globally.

Although agricultural intensification has generally increased crop yields, it also resulted in a range of environmental issues. These include increased erosion rates and declined soil organic carbon (SOC) stocks. In order to improve our understanding on how erosion impacts the overall SOC storage capacity of croplands, this study analyses the 3D distribution of SOC as a function of water and tillage erosion in a conventionally ploughed field in the Belgian silt loam region. We present a novel methodological framework to integrate the output of an advanced erosion model as a co-variate within a Digital Soil Mapping (DSM) approach with the objective to create detailed SOC maps. More precisely, we combined (i) the Water and Tillage Erosion Model and Sediment Delivery Model (WaTEM/SEDEM), simulating spatial patterns of soil erosion and sediment deposition due to water and tillage erosions, with (ii) a SOC sampling campaign, resulting in a SOC spatial distribution model that considers both types of erosion. The results show that, as compared to plateaus, SOC stocks are nearly half as large along eroding slopes (i.e. convex slope positions affected by tillage erosion and steep slopes affected by water erosion). Yet, they are up to twice as large in areas characterized by sediment deposition (i.e. concave positions due to tillage erosion and foot slope positions due to water erosion). Our results further show that tillage erosion has a significant influence on the SOC stocks in the top 0.7 m and in particular on the top 0.4 m. The influence of water erosion is less strong but mostly significant along the entire depth profile. Overall, this work demonstrates the relevance of considering different erosion processes when aiming to predict spatial patterns of SOC. Considering the top 1 m and a WaTEM/SEDEM application at a resolution of 10 m our 3D SOC modelling approach obtained a coefficient of determination (R2) of 0.62, a relative root mean square error (RRMSE) of 30.5 % and a relative mean absolute error (RMAE) of 26.0 %. While future work may likely lead to further improvements e.g. a more detailed SOC sampling network along the foot slopes and thalwegs in order to obtain a more spatially detailed prediction of the area characterized by depositions due to water erosion, we demonstrate the great potential of existing erosion and deposition models in doing so.

Iron (Fe) and methane (CH4) emissions play crucial roles in the carbon cycling of paddy field ecosystems. However, little is known about the effect and mechanism of Fe transformation on the CH4 emission from paddy fields, especially with different water management methods. In this study, the dynamic changes in CH4 emissions and different forms of Fe and soil organic carbon content were observed in paddy fields under flooded irrigation (FI) and water-saving irrigation (WSI) (including intermittent irrigation (II) and controlled irrigation (CI)). In addition, the structural equation model was used to clarify the response relationship between CH4 emissions and Fe ions in paddy fields. The results indicate that the cumulative CH4 emissions under WSI were reduced by 29.03–61.29%. The reasons are as follows (i) WSI increased the oxidation ability of Fe to CH4 by increasing the soil ferric iron (Fe3+) content in paddy fields; (ii) WSI could reduce the substrate utilization by methanogens by transforming the Fe oxide forms to increase the binding capacity of Fe to dissolved organic carbon, which reduced the CH4 production. In addition, compared with II, CI had higher soil Fe3+ content, Fe-bound organic carbon content, and lower acetate content, so it had lower CH4 production capacity. In summary, WSI promotes the direct oxidation of CH4 by Fe3+ in paddy fields and indirectly reduces CH4 production by facilitating the combination of Fe oxides with the substrates. These findings deepen our understanding of the mechanisms of CH4 emission in paddy fields and provide new insights for research on carbon sequestration and emission reduction in paddy field ecosystems.

Soil organic carbon (SOC) sequestration has become a critical component of climate change mitigation strategies, offering a natural and economically viable means to mitigate atmospheric CO2 levels. Current practices in SOC sequestration auditing face limitations due to the requirement for carbon permanence, which can discourage landholders from participating due to long-term commitments and uncertainties. We propose the concept of the Soil Carbon Tonne-Year as a new unit of measurement for assessing SOC sequestration, focusing on the time-integrated amount of carbon stored in the soil. Soil carbon tonne-year measures SOC stock across different operational soil carbon pools (such as Mineral Associated Organic Carbon and Particulate Organic Carbon), each with its own mean residence time. This approach, based on physical rather than economic or climatic metrics, aims to offer a more accurate, flexible, and realistic method of accounting for SOC. Our examples suggest that the Soil Carbon Tonne-Year approach could significantly enhance management flexibility, potentially increasing land value and leading to sustainable gains over the long term.

The dynamic soil properties for soil health (DSP4SH) is a Science of Soil Health Initiative that was designed to collect, process, and publicize scientifically rigorous datasets that inform sound indicators and interpretations. The Soil and Plant Science Division of the United States Department of Agriculture - Natural Resources Conservation Service (USDA-NRCS) and university cooperators collected a suite of standardized soil health metrics across eight states (Oregon, Washington, Kansas, Minnesota, Illinois, Connecticut, North Carolina, and Texas) within five soil survey regions (Northwest, North Central, Northeast, Southeast, and South Central). The DSP4SH database provides a substantial dataset of soil health metrics assessed. The dataset is composed of dynamic soil properties (DSP) data collected from each management system or ecological state represented by one to three independent plot replicates. Each plot has a minimum of three pedons. Nine groups from the DSP4SH monitoring network provided datasets used in developing the database. The submitted data includes 37 laboratory measured parameters, 60 variables of layer/horizon descriptions, 41 variables for laboratory analysis conducted at the Kellogg Soil Survey laboratory, and 12 variables for the management systems. An additional 31 variables were developed for site or plot description. Additional variables were developed to normalize the dataset. In preparation for DSP assessment, all tables (except for dataset from KSSL lab) were categorized by management system or ecological state. The categories were business as usual (BAU), the reference condition (Ref) and the soil health management (SHM). The overarching goal of DSP4SH phase 1 and 2 dataset publication is to promote increased accessibility, further analysis of the data, and overall understanding of the benefits of surveying dynamic soil properties for soil health.

Straw return is a vital soil amendment practice in Chinese upland soils, aiming to improve the soil organic carbon (SOC) stock. However, its impact on SOC stock depends on various fertilizer practices, leading to inconclusive results. To address this, we conducted a meta-analysis of 121 peer-reviewed publications to evaluate the effect of straw return combined with different fertilization practices including balanced NPK (BFS), unbalanced NP, NK, PK, or N fertilization (UFS), and no fertilization (NFS) in the wheat-maize cropping system. The results showed that straw return with BFS led to the highest increase in SOC stock (13.3%), followed by UFS (11.4%) and NFS (6.12%), compared to straw removal under the same fertilization conditions. BFS also significantly increased the soil C (15.9%), N (9.62%), and P (6.21%) contents, as well as the C:N (2.48%) and C:P (11.4%) ratios. In contrast, UFS resulted in higher C:N ratios (6.47%) and soil acidification, affecting the SOC stock during straw return. A structural equation model revealed that the presence of BFS positively influenced the association between SOC stock and soil stoichiometry (P ​12 ​g ​kg−1). Based on C sequestration efficiency, implementing straw return with BFS could lead to a higher rate of increase in SOC sequestration.

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.

This study investigated the relationships between the soil organic matter content (SOM), SOM thermal pools, soil properties, and tillage practices, on cropland soils of the central plateau of Switzerland. Soil samples were collected in 45 no-till and conventional tillage fields in five layers from 0 to 40 cm depth. Soil organic carbon content (SOC) and hydrocarbon compound (HC) pools were analysed with Rock-Eval® thermal analysis. In addition, the clay content was determined by sedimentation.The SOM contents were highest in 0–5 cm of no-till soils and the plough pan of tilled soils. SOM was less oxygenated and showed lower degradation under conventional tillage then no-tillage. The proportion of the thermo-stable pool was mainly explained by the SOC:clay ratio, regardless of tillage practices. Below a SOC:clay ratio of 0.08, all pools were decreasing nearly equally with decreasing SOC. Above this ratio, thermo-stable pools increased only slightly, while thermo-labile pools increased linearly with SOC:clay on the full SOC:clay range. The 0.08 SOC:clay threshold in the thermo-stable pools content corresponded to the lower threshold of structure stability determined for Swiss and UK soils, below which, on average, severely degraded structures are observed in the field. These findings suggest that a large proportion of thermo-labile pool is necessary for acceptable to high soil structure quality, to protect the thermo-stable forms from degradation, and to reach the carbon sequestration potential of the soil.

Soil organic carbon (SOC) represents the largest carbon reservoir in terrestrial ecosystems. Therefore, understanding how to enhance SOC is crucial for the global carbon cycle and atmospheric CO2 removal. While there has been reported on SOC accumulation following forest restoration efforts, changes in plant lignin and microbial necromass across soil fractions and depths remain unclear. To address this gap, we investigated the SOC-standardized concentrations of lignin phenols and amino sugars in bulk soil and soil fractions (particulate organic matter (POM) and mineral-associated organic matter (MAOM)) during subtropical forest restoration, as tracers for change in plant lignin components and microbial necromass respectively. Our findings indicate that forest restoration does not affect SOC concentrations, but does result in changes in plant lignin and microbial necromass. Specifically, in the subsoil, lignin phenols and amino sugars concentrations in bulk soil and soil fractions rise significantly with restoration time, with lignin phenols and amino sugars concentrations in bulk soil rising by 29.6% and 53.0%, respectively. This suggests that as forests recover, lignin and microbial necromass have a higher contribution to SOC accumulation in the subsoil, while the contribution of low-molecular plant-derived organic matter decreases. The relative change rate of lignin phenols (relative to SOC) is greater than that of amino sugars in POM in the topsoil, while in the subsoil, the relative change rate of amino sugars in MAOM is greater than that of lignin phenols. These results indicate that lignin in POM declines more rapidly than amino sugars during restoration, whereas in MAOM, amino sugars accumulate faster than lignin. Overall, these findings provide important insights into the regulation of SOC accumulation during forest restoration by the combination of plant lignin and microbial necromass in various stabilization pathways.

Extensive use of per- and polyfluoroalkyl substances (PFAS) in various industrial and consumer products in the past has led to their widespread distribution in the environment. PFAS contamination has become a major environmental and public health threat worldwide, especially in communities impacted by industrial, commercial, and military activities. In 2021, twelve soil samples were collected from three distinct site types in Brevard County, Florida, based on community concerns: background sites, primary-source sites, and secondary-source sites. These sites comprised samples collected from both residential and industrial/commercial areas. Ultra-high performance liquid chromatography-tandem mass spectrometry was used to identify and quantify PFAS in the samples. The results show that PFAS were present in all soil samples, with the lowest and the highest concentrations being in background and primary sites, respectively. Total PFAS concentrations in both primary and secondary sites were generally one-order of magnitude greater than those reported in background sites. Perfluorooctanesulfonic acid concentrations were the most predominant among the 34 species of PFAS detected in the samples, with concentrations ranging from 0.04 to 2.63 ​ng/g. Analysis of variance of PFAS data reveals significant difference among study sites, with greater diversity and concentrations near primary sources followed by secondary sites and background sites. The results also demonstrate that long-chained PFAS are significantly more abundant in these soils than short-chained PFAS. Overall, our results should help prioritize future sampling locations for a rapid and systematic identification of PFAS in soils.

National soil organic carbon (SOC) maps are essential to improve greenhouse gas accounting and support climate-smart agriculture. Large-scale SOC models based on wall-to-wall soil information from remote sensing remain a challenge due to the high diversity of natural soil conditions and the difficulty of accounting for the spatial location of the soil samples. In this study, we tested if the implementation of local ensemble models (LEM) can be used to improve the SOC predictions from Landsat-based soil reflectance composites (SRC) for Germany. For this, we divided the research area into 30 times 30 km tiles and calculated local generalized linear models (GLM) based on random, nearby observations. Based on the GLMs, local SOC maps were predicted and aggregated using a moving window approach. The local variable importance was analyzed to identify spatial dependencies in the correlation between the SRC and SOC. For the final SOC map, a Random Forest (RF) model was trained using the aggregated local SOC predictions, the SRC, and a full set of training samples from the agricultural soil inventory. The results show that the LEM was able to improve the accuracy (R2 = 0.68; RMSE = 5.6 g kg−1), compared to the maps based on a single, global model (R2 = 0.52; RMSE = 6.8 g kg−1). The local variable importance of the spectral bands showed clear spatial patterns throughout the research area. Differences can be explained by the local soil conditions, influencing the correlation between SOC and the spectral properties. Compared to the widely adopted integration of distance covariates such as geographical coordinates, the LEM was able the reduce the spatial autocorrelation to a greater extent and to improve the prediction accuracy, especially for underrepresented SOC values. The LEM presents a new method to integrate spatial information and increase the interpretability of DSM models.

Urbanisation induces nitrogen (N) enrichment in tropical soils. This enhances soil acidification, leading in turn to a decrease in effective cation exchange capacity (CECe) and base saturation (BSe). Here, we investigated if and how these effects of urbanisation-induced N inputs affect the soils of tropical urban agroecosystems. Our aim was to quantify the impact of urbanisation intensity (UI) on total N (TN) contents and stocks, and to detect possible adverse N effects on soil pH, CECe, and exchangeable bases in rainfed urban arable soils of Kumasi, a tropical city in Ghana, West Africa. We analysed 618 topsoil samples (0–10 cm) from arable fields within the urban perimeter, categorised into four classes forming a gradient from low to high UI. For baseline comparison, 9 samples each from both rural arable fields and an urban forest were included. Urban arable soils generally had higher TN levels than their rural counterparts. Among the urban arable soils however, TN levels decreased with increasing UI, possibly as crop production and related N export through harvest intensified. Conversely, increasing UI correlated with increased levels of exchangeable Ca2+, K+, and Na+. No adverse N effect on soil pH, CECe, and BSe was detected. This could be explained with the strong TN-SOC relationship (R2 ≥ 0.9), and an increasingly strong relationship between exchangeable bases and TN and SOC (R2 = 0.5–0.8) with increasing UI. These relationships suggested a major common source of TN, SOC, and exchangeable bases. As the soils are neither chemically fertilised nor limed, these findings supported earlier studies that this source is provided by dumped biodegradable household waste, charred matter, ash, and construction materials. We concluded that soil N enrichment through these materials in Kumasi is accompanied by abundant bases in the same materials, which overcompensate any theoretic acidification. Our findings imply that not only can the fertility of tropical urban arable soils in cities like Kumasi be maintained, but also that the crops and ground water may not be at risk from the mobilisation of trace elements and heavy metals due to the hindered soil acidification.

Soil organic carbon (SOC), texture, clay/organic carbon (OC) ratio, and extractable phosphorus are among the key soil descriptors representing the physical, chemical, and biological properties. However, analyzing these soil properties using conventional methods of analysis is time-consuming and often involves the use of hazardous chemicals. Therefore, scaling these methods to analyze more samples covering large geographical distributions within a reasonable turn-around time has become a challenge. In this study, two rapid techniques, namely laser-induced breakdown spectroscopy (LIBS) and visible and near-infrared spectroscopy (vis-NIRS), were compared for the prediction of SOC, texture, extractable phosphorus, and clay/OC ratio. A total of 896 samples collected from 23 European countries from the Land Use/Cover Area frame Survey (LUCAS) database were used to represent a wide distribution in terms of soil texture and geographical coverage. The data was manually split into calibration and validation samples while ensuring a representative geographical distribution in the calibration and validation set for the selected samples. Partial least square regression (PLSR) models were developed for both LIBS and vis-NIRS and compared based on prediction accuracy. LIBS outperformed vis-NIRS in predicting SOC, sand content, and clay/OC ratio, with root mean square error of prediction (RMSEP) values of 1.79 %, 12 %, and 7.69, respectively. Conversely, vis-NIRS yielded slightly higher RMSEP values for SOC, sand content, and clay/OC ratio predictions at 1.88 %, 15 %, and 8.13, respectively. Both LIBS and vis-NIRS performed similarly in predicting clay content, with an RMSEP of 6 %. However, there was a low predictive ability for silt and phosphorus concentration, as shown by the weak correlations (R2 0.05). However, there was a significant difference between the predicted sand content (p = 0.011) and clay/OC ratio (p

Soil organic carbon (SOC) is important in the global carbon cycle. Accurate estimation of SOC content in cultivated land is a prerequisite for evaluating the carbon sequestration potential and quality of soils. However, existing SOC prediction studies based on hyperspectral remote sensing neglect the spectral response of the physical properties of surface soil, leading to inadequate model generalization. With the exponential growth of remote sensing data, the development of pixel-level soil spectral correction methods based on multi-source remote sensing data has become an interesting and challenging topic. This method aims to minimize the effect of soil physical properties on spectra, thus addressing the poor spatiotemporal transferability of SOC prediction models due to uncertain variations in surface soil physical properties. In this study, a soil spectral correction strategy is constructed using satellite hyperspectral image (HSI) and synthetic aperture radar (SAR) images through multi-order polynomial regression and convolutional neural networks. This strategy considers soil physical variables such as soil moisture (SM) content and root mean square height (RMSH) of soil surface roughness. The soil spectral correction model and SOC content prediction model were established using 80 soil samples collected from Site 1. Afterward, the performance and transferability of both models were verified using the remaining 25 samples from Site 1 and 50 samples from Site 2. The results showed that: 1) The effect of SM and RMSH on the soil pixel spectrum can be significantly reduced after correcting HSI using soil spectral correction strategy. The correlation coefficients between the corrected pixel spectrum and the ground-based spectrum increase by over 60 % compared with those between the original spectrum and the ground-based spectrum. 2) Soil spectral correction improves the prediction accuracy and mapping capability of HSI for SOC content, with the highest RP2 of 0.743 and RMSEP of 3.455 g/kg at Site 1. 3) Compared with the original HSI-based SOC prediction model, the soil spectral correction strategy based on multi-order polynomial and convolutional neural network reduced the RMSEP of SOC prediction results at Site 2 by 5.082 g/kg and 5.454 g/kg, and the RP2 increased by 0.390 and 0.409, respectively. 4) When predicting SOC content from raw HIS, SM and RMSH contribute to more than 60 % of the bias, with SM having a larger bias than RMSH. The findings of this study emphasize the influence of soil physical properties on SOC prediction and contribute to the existing research on SOC mapping using HSI and SAR data.

Long-term application of organic and mineral fertilizers can lead to changes in the elemental composition of agroecosystem components. Both the levels of nutrients and potentially toxic elements can change, as can the potential for these elements to be available to plants through changes in soil properties. Soil and plant samples of two species (pea Pisum sativum L. and oat Avena sativa L.) were collected from plots of a long-term field experiment on the application of mineral and organic fertilizers and their combinations to gray forest soils in the Vladimir region, Russia. Soil samples from the 0-20 and 20-40 cm layers were subjected to acid digestion to determine total element content. Mobile forms of elements were extracted from topsoil samples using acetate-ammonium buffer (pH 4.8). Sample preparation of pea and oat plant organs (stems, leaves, pods/ears) included sample digestion in a microwave sample digestion system ETHOS EASY (Milestone, Italy). The elemental composition of the samples was determined by inductively coupled plasma optical emission spectrometry (ICP-OES) using Agilent 5800 ICP-OES (Agilent Technologies, USA). The dataset includes concentration data for 34 elements, including rare earth elements, in these samples collected in 2021. The dataset also contains general agrochemical characteristics of soils of the experimental groups: pH of water and salt suspension, organic carbon content, mobile forms of phosphorus. The data can be valuable to researchers developing fertilizer application systems and modeling changes in the elemental composition of agroecosystems.

The soil organic carbon (SOC) to clay-sized particles ratio (SOC/clay) has recently been selected as an indicator of the soil organic matter status in managed mineral soils within the framework of the European Soil Monitoring Law proposal. This indicator was initially developed to predict soil structural quality in a local study in Switzerland and was subsequently tested at national scales in England and Wales, and in Germany. In this study, we evaluated if the SOC/clay ratio was relevant to assess the structural quality of soils at the national scale in mainland France. We additionally evaluated its variant, SOC/(silt

Soils are a major player in the global carbon (C) cycle and climate change by functioning as a sink or a source of atmospheric carbon dioxide (CO2). The largest terrestrial C reservoir in soils comprises two main pools: organic (SOC) and inorganic C (SIC), each having distinct fates and functions but with a large disparity in global research attention. This study quantified global soil C research trends and the proportional focus on SOC and SIC pools based on a bibliometric analysis and raise the importance of SIC pools fully underrepresented in research, applications, and modeling. Studies on soil C pools started in 1905 and has produced over 47,000 publications (>1.7 million citations). Although the global C stocks down to 2 m depth are nearly the same for SOC and SIC, the research has dominantly examined SOC (>96 % of publications and citations) with a minimal share on SIC (

Mountain ecosystems exhibit rapid changes in vegetation cover and climate conditions along elevation gradients, offering an ideal natural experiment to examine variations in the molecular composition of soil organic carbon (SOC) responsing to environmental change. We examined molecular composition, diversity, and network complexity of SOC using the pyrolysis-gas chromatography/mass spectrometry technology and the Shannon diversity index (SHDI) in Changbai Mountain (CBS). Results indicated that molecular composition of SOC differed greatly across different vegetation zones, which were predominated by mixed coniferous and broad-leaved forests (HJL), mixed coniferous forests (ZYL), birch forest covered (YHL), and tundra vegetation (TYD). ZYL had remarkably more abundant moieties with aromatic structures, 36% on average, than those in HJL, TYD, and YHL, 18.31%, 12.75% and 16.51% on average, respectively. O-alkyl, alkyl, and terpene compounds showed an increasing trend, whereas aromatic, lignin and nitrogen-containing compounds exhibited a notable decrease with elevation to a significant level. Either alpha diversity or beta diversity of SOC varied significantly among TYD, YHL, ZYL, and HJL, with HJL exhibiting the lowest alpha diversity. SOC from ZYL was the most complex, showing the highest number of edges and graph density. Leaf litter elements greatly regulated the molecular diversity and network complexity of SOC. Positive correlations between SHDI and SOC contents, as well as ratios of SOC to total iron, suggested that high molecular diversity promoted SOC persistence in mountain ecosystems.