Low Soil Organic Carbon Content Research Articles

A Properly Chosen Rate of NPK Fertilizers Has a Positive Effect on C Sequestration in Sandy Soils in the Conditions of a Changing Climate

Abstract Soil organic carbon (SOC) plays a significant role in climate change. Its content can be modified by soil management practices, however, the effect of mineral fertilization on SOC is not clear. For this reason, a long-term effect of gradually increasing rates of NPK fertilizers on changes in soil organic carbon (SOC) in bulk soil and in water-stable aggregates (WSA) in soils with sandy loam and loamy sand texture at two experimental sites (Skierniewice, Poland, and Dražovce, Slovakia) was quantified. In both sites, soil samples were collected from the following treatments: NF – no fertilization, NPK1 and NPK2 – 1st level and 2nd level of NPK fertilization, respectively. The results showed that 100-year long application of NPK1 increased total carbon (TC) and SOC content by 24%, while NPK2 decreased it by 5% compared to NF at the Skierniewice site. The content of water-stable macroaggregates (WSAma) increased because of NPK application. In NPK1, the content of WSAma was higher and the content of water-stable microaggregates (WSAmi) was lower than in NPK2 or NF. However, as a result of NPK application, the content of agronomically favorable WSAma in size fraction 0.5–3 mm was reduced by 8 and 24% in NPK1 and NPK2, respectively, compared to NF. Overall, SOC in WSAma was lower than in bulk soil. The SOC in WSAma in NF, NPK1 and NPK2 treatments was 6.51, 7.77 and 5.89 g.kg−1, respectively. Similar tendency of SOC in WSAma 0.5–3 mm was observed (NF: 6.12 g.kg−1, NPK1: 7.35 g.kg−1, and NPK2: 6.88 g.kg−1). The SOC in WSAmi in NF, NPK1 and NPK2 was 8.33, 7.39 and 7.24 g.kg−1, respectively. At Dražovce site, TC content decreased significantly due to the graded rates of NPK, not because of SOC mineralization but as a result of carbonate dissolution for a period of 14 years. The carbonate content decreased from 20 g.kg−1 in NF to 6.5 g.kg-1 in NPK1 and 3.0 g.kg-1 in NPK2, while SOC did not change significantly: (NF: 23.8 g.kg−1, NPK1: 25.9 g.kg−1, and NPK2: 23.4 g.kg−1). In NPK1, the WSAma content was reduced significantly when compared to NPK2 and NF treatments. No significant difference was observed between NF and NPK2. On the contrary, the content of WSAma 0.5–3 mm significantly increased when compared to NF and NPK1. No difference was observed between NF and NPK1. Lower SOC content was found in WSA than in the bulk soil. Overall, higher SOC content was observed in WSAma when compared with WSAmi. The application of NPK1 and NPK2 increased SOC in WSAma as well as in WSAma 0.5–3 mm. The effect was more significant in NPK1 than NPK2 treatments when compared to NF.

Warming persistently stimulates respiration from an arable soil over a decade, regardless of reduced summer precipitation

Climate change factors alter soil microbial biomass and respiration, which represent two key parameters of carbon (C) cycling in soils. While these interrelations are increasingly well described for soils in natural ecosystems, long-term studies conducted in temperate agricultural ecosystems do rarely exist. Here we used the ‘Hohenheim Climate Change Experiment (HoCC)’ to describe the response of microbial biomass and respiration to interacting effects of climate change factors. We were interested in whether effects would be season specific, and if ultimately, climate change factors would affect the total soil organic carbon (SOC) content in the arable soil. The HoCC experiment is an agroecosystem where soils were permanently warmed for one decade, and summer precipitation amount and frequency were reduced. We found that soil respiration was not affected by reduced summer precipitation, but strongly increased by warming. This increase was not season specific and persisted over the years. Further, warming continuously enhanced labile C availability, while microbial biomass did not differ between warmed and ambient temperature soils. Therefore, soil respiration stimulated by warming was independent of microbial biomass, and accordingly, we found an increase of the metabolic quotient. Despite the warming induced increase in soil respiration, no indications for a loss in SOC content of the arable soil was detected. These results suggest that in an arable soil with overall low SOC content, the persisting stimulation of respiration due to warming is driven by an agroecosystem specific labile C pool dynamic.

The trend of changes in soil organic carbon content in Poland over recent years

The article analyzes soil organic carbon (SOC) content of in Poland from 2015 to 2021. The research aims to determine SOC levels and their dependence on soil agronomic categories and drought intensity. Soil samples from 1011 farms across 8 Polish voivodships were collected for analysis, all from the same agricultural plots. SOC determination was conducted using the Tiurin method. The results indicate a low SOC content nationwide (0.85-2.35%). Heavy soils exhibited higher SOC accumulation compared to light soils. Moreover, significant drought impact led to decreased SOC content in affected regions. Scientific evidence underscores a declining trend in organic carbon stock within agricultural soils, attributed to natural soil changes and unsustainable management practices. This decline is concerning given the crucial role of SOC in soil health, quality, and crop productivity. Therefore, it is imperative to monitor and address areas with low SOC levels to enhance SOC abundance. Furthermore, when used as a whole-cell biocatalyst in a low-cost upflow MFC, the Morganella morganii-rich SF11 consortium demonstrated the highest voltage and power density of 964.93±1.86 mV and 0.56±0.00 W/m3, respectively. These results suggest that the SF11 bacterial consortium has the potential for use in ceramic separator MFCs for the removal of penicillin and electricity generation.

Effects of Organic Matter Addition on Soil Carbon Contents, CO2 Emissions, and Bacterial Compositions in a Paddy Field in South China

Increasing soil organic carbon (SOC) contents and reducing carbon dioxide (CO2) emissions in paddy soil fields can result in positive impacts on climate change mitigation and soil quality. However, SOC accumulation and its microbial driving factors under enhanced fertilization strategies (e.g., organic matter application) are still unclear. Therefore, we investigated the effects of organic matter addition on SOC variations, CO2 fluxes, and their relationships with soil bacterial compositions and functions through a 6-year fertilizer experiment in rice fields involving two fertilization types, namely chemical fertilizer (NPK) and chemical fertilizer combined with organic matter (NPK+OM). The results showed significantly higher and lower SOC contents (p < 0.05) in the 10–20 cm soil layer under the NPK+OM treatment before rice transplanting and after rice harvest, respectively, than those under the NPK treatment. The lower SOC contents after rice harvest might be due to the great nutrient consumption, resulting in higher rice yields in the NPK+OM than those in the NPK treatment by 6.68 to 32.35%. Compared with NPK, NPK+OM reduced the in-situ CO2 fluxes by 38.70–118.59%. However, the ex-situ SOC mineralization rates were not affected by NPK+OM in the 0–10 and 10–20 cm soil layers. The 16S rRNA sequence indicated a significant increase in the abundance of non-singleton amplicon sequence variants (ASVs) in the NPK+OM treatment scenario compared to those in the NPK treatment scenario. The top three most important soil bacterial phylum influenced by NPK+OM were LCP-89, BRC1, and Rokubacteria in April, as well as Firmicutes, Nitrospinae, and BRC1 in July. Soil Actinobacteria was negatively correlated with the SOC contents in April and July. The results of the present study demonstrate the economic and ecological benefits of the organic matter addition in rice production, as well as the contribution of soil bacteria to SOC accumulation and CO2 emission reduction.

A Soil Analysis Approach to Assessing Potential Loss of Productive Lands Under Agricultural Land Conversion

Land provides physical space and is usually required for various sectoral developments needed to meet the needs of increasing population. Land is a finite natural resource; thus, conflict arises over land use and development. The strategic location of the municipality of Pura in Tarlac province, Philippines within the urban beltway of Central Luzon and the recent opening of the Tarlac–Pangasinan–La Union Expressway provide Pura excellent opportunities for urban and industrial development. However, the precursor to this is agricultural land conversion (ALC), which can change or reduce in the area of productive lands. This paper assessed the degree of productivity of the agricultural lands in the study area that are predisposed to ALC using FAO’s land suitability framework and the revised Storie index for soil productivity. Soil survey and composite soil sampling at 20 cm depth in the selected 34 sampling points were done to analyze the relevant soil physical and chemical properties. Five soil mapping units (SMU) were grouped based on the soil surface texture. The results show that the SMUs are only marginally suitable (S3f) for producing rice and other crops due to their current low soil organic carbon content. However, these SMUs can be highly suitable (S1) for crop production with appropriate soil management. Using the Storie index, the entire tract of land of Pura has an index rating of 58 percent, which corresponds to a grade 3 soil suitable for planting a number of crops with expected good results. The results of the land suitability evaluation and soil productivity assessment further show that the land in the municipality of Pura is productive, and thus can benefit both agricultural production and ALC. As such, whichever spatial strategy or policy direction for ALC that the municipal government chooses to adopt, the municipality of Pura will lose productive land.

Soil Quality Indicators under Different Smallholder Managed Cropping and Landuse Practices in Abuja, Nigeria

There are limited reports about the impacts of smallholder-managed cropping and land-use practices (CLUPs) on soil quality, especially in developing countries. This study investigates the impact of six different Cropping Land Use Practices (CLUPs) on soil quality parameters in Abuja, Nigeria, focusing on sesame mono-cropping (SM), guinea corn mono-cropping (GCM), yam mono-cropping (YM), maize + yam mixed cropping (MYM), maize mono-cropping (MM), and natural forest (NF). The objectives are to determine Soil Organic Carbon (SOC), Total Nitrogen (TN), and pH values in the six CLUPs and assess differences under various CLUPs and soil depths. The study conducted in the University of Abuja Permanent Site, covers 11,000 hectares and analyzes soil samples from three replicate plots per CLUP, considering topsoil (0-15cm) and subsoil (20-30cm). The results indicate slightly acidic soils with low SOC and TN contents. Significant differences in SOC, TN, and C/N ratio are observed among cropping systems, with mixed cropping (MYM) promoting higher SOC. The C/N ratio is consistently low across CLUPs, indicating accelerated decomposition. While intra-plot soil heterogeneity is low, significant declines in soil quality indicators are noted under cropping systems compared to the natural forest. The study recommends site-specific, sustainable land management practices tailored to each cropping system. Encouraging organic matter additions, such as using animal and farmyard manure, is proposed to enhance soil fertility and reverse degradation trends. The findings contribute to understanding how diverse cropping practices impact soil quality, providing valuable insights for sustainable land management in the Abuja region.

Regional spatial variability of soil organic carbon in 0–5 m depth and its dominant factors

Soil organic carbon (SOC) stored in deeper soils is becoming increasingly significant in carbon management and carbon cycle especially in the face of climate and land use alterations. However, spatial variability of deep SOC at a regional scale and its related dominant factors are rarely investigated. To ascertain the vertical distribution and regional spatial variations in the SOC content of deep soil, 1648 soil samples were collected from 67 sites across the Chinese Loess Plateau (CLP) at a depth of 500 cm. Results showed that the mean SOC content of three land use types were recorded as follows: cropland (2.93 ± 0.29 g kg−1) > forest (2.56 ± 0.24 g kg−1) > grassland (2.39 ± 0.29 g kg−1), and the vertical distribution of soil profile from 0 to 500 cm was similar. Furthermore, a significant alteration in SOC content was observed in the soil layer of 0–180 cm among the three land use types (p < 0.05). Spatial distribution maps of SOC content indicated that there was a central region of the northern CLP with low SOC content, which corresponds to the eolian sand area in Ordos. Both the spatial variations and influencing factors of SOC content were found to be depth-dependent, but soil texture and temperature were identified as the dominant factors affecting the 0–500 cm soil profile of the CLP. Clarifying this information is helpful for understanding content level and variation of deep SOC, which can provide a reference for carbon sequestration in deep soil and developing sustainable SOC management for the CLP and other related ecological regions.

Relationship between carbon pool changes and environmental changes in arid and semi-arid steppe—A two decades study in Inner Mongolia, China

As the arid and semi-arid grassland with the most extensive distribution area in northern China, the carbon stored in Inner Mongolia (IM) grassland is highly susceptible to environmental changes. With the global warming and drastic climate changes, exploring the relationship between carbon pool changes and environmental changes and their spatiotemporal heterogeneity is necessary. This study estimates the carbon pool distribution of IM grassland during 2003–2020 by combining the measured below ground biomass (BGB) dataset, measured soil organic carbon (SOC) dataset, multi-source satellite remote sensing data products, and random forest regression modeling method. It also discusses the variation trend of BGB/SOC and its correlation with critical environmental factors, vegetation condition factors and drought index. The results show that the BGB/SOC in IM grassland was stable during 2003–2020, with a weak upward trend. The correlation analysis reveals that high temperature and drought environment were unfavorable for developing vegetation roots and would lead to a decrease in BGB. Furthermore, temperature rise, soil moisture decrease, and drought adversely effected grassland biomass and SOC in areas with low altitude, high SOC density, suitable temperature and humidity. However, in areas with relatively poor natural environments and relatively low SOC content, SOC was not significantly affected by environmental deterioration and even showed an accumulation trend. These conclusions provide directions for SOC treatment and protection. In areas where SOC is abundant, it is important to reduce carbon loss caused by environmental changes. However, in areas with poor SOC, due to the high carbon storage potential of grasslands, carbon storage can be improved through scientifically managing grazing and protecting vulnerable grasslands.

Digital Mapping of Soil Organic Carbon Using UAV Images and Soil Properties in a Thermo-Erosion Gully on the Tibetan Plateau

Thermo-erosion gullies (TGs) are typical thermokarst features in upland permafrost; the soil organic carbon (SOC) of TGs has an important influence on soil quality in cold regions. The objectives of this study were to estimate the spatial distribution of SOC content in a typical TG on the northeastern Tibetan Plateau in China by using soil properties from seven different TGs and covariates from unmanned aerial vehicle (UAV) images, and to characterize the SOC content changes in four representative landscape regions (NO-Slumping, Slumping1, Slumping2, and Slumped) within this typical TG. The support vector machine (SVM) was the optimal machine learning algorithm for SOC content prediction, which explained 53.06% (R2) of the SOC content variation. Silt content was the most influential factor which demonstrated a positive relationship with SOC content in different TGs. In addition, the SOC content in the TGs was related to the landscapes. Severe Slumping (Slumping2: 150.79 g·kg−1) had a lower SOC content than NO-Slumped (163.29 g·kg−1) and the initial slumping stage (Slumping1: 169.08 g·kg−1). The results suggested that SVM was an effective algorithm to obtain a profound understanding of the SOC content over space, while future research needs to pay more attention to the SOC content distribution in the different TGs.

Prevalence and drivers of abrupt vegetation shifts in global drylands

The constant provision of plant productivity is integral to supporting the liability of ecosystems and human wellbeing in global drylands. Drylands are paradigmatic examples of systems prone to experiencing abrupt changes in their functioning. Indeed, space-for-time substitution approaches suggest that abrupt changes in plant productivity are widespread, but this evidence is less clear using observational time series or experimental data at a large scale. Studying the prevalence and, most importantly, the unknown drivers of abrupt (rather than gradual) dynamical patterns in drylands may help to unveil hotspots of current and future dynamical instabilities in drylands. Using a 20-y global satellite-derived temporal assessment of dryland Normalized Difference Vegetation Index (NDVI), we show that 50% of all dryland ecosystems exhibiting gains or losses of NDVI are characterized by abrupt positive/negative temporal dynamics. We further show that abrupt changes are more common among negative than positive NDVI trends and can be found in global regions suffering recent droughts, particularly around critical aridity thresholds. Positive abrupt dynamics are found most in ecosystems with low seasonal variability or high aridity. Our work unveils the high importance of climate variability on triggering abrupt shifts in vegetation and it provides missing evidence of increasing abruptness in systems intensively managed by humans, with low soil organic carbon contents, or around specific aridity thresholds. These results highlight that abrupt changes in dryland dynamics are very common, especially for productivity losses, pinpoint global hotspots of dryland vulnerability, and identify drivers that could be targeted for effective dryland management.

137Cs-based analysis of soil redistribution in the Integrated Watershed Management intervention area in northern Ethiopia

Spatial patterns of trace isotope cesium-137 (137Cs) can be strongly correlated with soil movement in a landscape, thereby allowing assessments of soil erosion and deposition processes, based on these patterns. Our study assessed how soil organic carbon (SOC) stocks were related to the redistribution of 137Cs in prevalent forest and agricultural land-use systems in a north Ethiopian watershed undergoing land rehabilitation. Composite soil samples were collected from 0 to 5, 5–10, 10–20, and 20–30 cm layers of exclosures, croplands, rangelands, and bare lands at different slope positions. Reference samples were collected from a vegetated hilltop with no anthropogenic soil disturbance history. The use of 137Cs analysis was not effective in revealing SOC dynamics in croplands, which was probably a result of soil tillage and generally low SOC contents. Otherwise, 137Cs tracing was a suitable method for the soil redistribution assessment in the tropical environment, owing to sufficient amount of the isotope detected following the original fallout. Comparisons of 137Cs contents between the reference and sampling sites revealed a gap in 137Cs inventories in 87% of all locations, thereby indicating severe erosion, especially on the backslopes. Some of the examined samples, mostly from rangelands and exclosures, exhibited 137Cs activities that were higher than the reference, signifying these sites as depositional areas for soil material eroded elsewhere. Soil movements indicated by 137Cs profiles, were particularly substantial in rangelands, where 137Cs stock varied significantly according to the slope positions and soil depths. Notably, significant correlations between SOC stocks and 137Cs levels in exclosures suggested that both the prevention of soil loss and the capture of soil eroded from degraded sites fortified SOC accumulation. This suggests that not only the increase in vegetation cover but also the soil deposition was an attribute of the land rehabilitation process.

Using isotope pool dilution to understand how organic carbon additions affect N2 O consumption in diverse soils.

Nitrous oxide (N2O) is a formidable greenhouse gas with a warming potential ~300× greater than CO2. However, its emissions to the atmosphere have gone largely unchecked because the microbial and environmental controls governing N2O emissions have proven difficult to manage. The microbial process N2O consumption is the only know biotic pathway to remove N2O from soil pores and therefore reduce N2O emissions. Consequently, manipulating soils to increase N2O consumption by organic carbon (OC) additions has steadily gained interest. However, the response of N2O emissions to different OC additions are inconsistent, and it is unclear if lower N2O emissions are due to increased consumption, decreased production, or both. Simplified and systematic studies are needed to evaluate the efficacy of different OC additions on N2O consumption. We aimed to manipulate N2O consumption by amending soils with OC compounds (succinate, acetate, propionate) more directly available to denitrifiers. We hypothesized that N2O consumption is OC‐limited and predicted these denitrifier‐targeted additions would lead to enhanced N2O consumption and increased nosZ gene abundance. We incubated diverse soils in the laboratory and performed a 15N2O isotope pool dilution assay to disentangle microbial N2O emissions from consumption using laser‐based spectroscopy. We found that amending soils with OC increased gross N2O consumption in six of eight soils tested. Furthermore, three of eight soils showed Increased N2O Consumption and Decreased N2O Emissions (ICDE), a phenomenon we introduce in this study as an N2O management ideal. All three ICDE soils had low soil OC content, suggesting ICDE is a response to relaxed C‐limitation wherein C additions promote soil anoxia, consequently stimulating the reduction of N2O via denitrification. We suggest, generally, OC additions to low OC soils will reduce N2O emissions via ICDE. Future studies should prioritize methodical assessment of different, specific, OC‐additions to determine which additions show ICDE in different soils.

Assessing the influencing factors of soil susceptibility to wind erosion: A wind tunnel experiment with a machine learning and model-agnostic interpretation approach

Soil wind erosion is a worldwide environmental concern. This study explored the feasibility of using a machine learning (ML) algorithm with a model-agnostic interpretation approach to infer the mechanism by which multiple features influence the soil susceptibility to wind erosion. Arenosols were collected from the Ulanbuhe Desert, China, and soil samples with different treatments, including wet-dry and freeze-thaw processes, were used in a wind tunnel experiment and provided a basic dataset for modelling. The Shapley additive explanations (SHAP) method was used to provide global and local interpretations for the ML model. The results showed the following. (1) The mean particle size tended to decrease and the soil organic carbon (SOC) and soil calcium carbonate (CaCO3) contents tended to increase in the transition from mobile to fixed sand dunes. (2) The ML models presented a better performance than the interpretable models, and the random forest model (R2 value of 0.898 on the test dataset) was adopted. (3) All the features except the SOC content showed monotonic or nearly monotonic relationships with the wind erosion rate. Relatively low SOC contents and high particle sizes tended to show positive interaction effects with wet-dry and freeze-thaw processes. An SOC content of 2.46 g kg−1 was deemed a threshold, above which SOC worked to resist wind erosion. ML algorithms and model-agnostic interpretation methods have the potential to improve the reliability and flexibility of inferring the mechanism by which soil is susceptible to wind erosion and thus deserve popularization in future studies.

Optimizing Carbon Sequestration Through Cover Cropping in Mediterranean Agroecosystems: Synthesis of Mechanisms and Implications for Management

Hot and dry Mediterranean ecoregions are characterized by low soil organic carbon content and large potential to become carbon sink when appropriately managed. Soil carbon sequestration may also play an important role in improving the resilience of these vulnerable agroecosystems to increasingly drastic impacts of global climate change. One agricultural practice that aims to increase soil organic carbon stocks, among other beneficial outcomes, is the use of cover crops. Although cover crops can increase soil organic carbon content, recent studies have observed that cover crops may lead to lower soil carbon stocks when considering co-management strategies, especially at greater soil depths. In this review, we outline the current paradigm of soil organic carbon dynamics and aim to apply our current understanding of soil carbon sequestration processes to cover crop management. We review how cover crop practices such as cover crop species selection, growth duration, and termination methodologies may impact soil organic matter sequestration and stabilization processes and provide insights to direct future research and inform cover crop management for C sequestration in Mediterranean agroecosystems.

Influence of parent material on soil chemical characteristics in a semi-arid tropical region of Northeast Brazil.

Soil parental material is one of the main factors that influence pedogenesis. Several studies evaluated the relationship between the parent material and soil chemistry, but few studies have assessed such a relationship in semi-arid tropical regions. This investigation was carried out to assess the effect of different parent materials on the chemical composition and available concentrations of macronutrients and micronutrients, including potentially toxic elements (Cd, Cr, Ni, and Pb) in soils in a semi-arid tropical setting. The chemical composition of the soils inherited the geochemical signature of their parent materials. Quartz sandstones, augen gneisses, and peraluminous granites exhibited the lowest reservoirs of plant nutrients and formed sandy, acid, and infertile soils. On the other hand, alkaline soils and soils with high concentrations of nutrients formed on ultramafic rocks (harzburgite), marble, and anorthosite. The pH, clay content, and CEC were the main attributes of the soils governing the availability of macro and micronutrients. The low soil organic carbon contents did not influence the availability of the nutrients. The parent material also influenced the soil texture. Parent materials that are richer in silica formed more sandy soils. The availability of Cd, Cr, and Pb in soils was low; however, the elevated Ni concentrations of soils derived from ultramafic rocks may pose risks to the environment and human health. Cluster and discriminant analyses were used to discriminate the natural fertility of soils. These results are useful for the agro-pedological zoning of the Brazilian semi-arid tropical region and for land use planning.

Estimation of Soil Organic Carbon Content in the Ebinur Lake Wetland, Xinjiang, China, Based on Multisource Remote Sensing Data and Ensemble Learning Algorithms.

Soil organic carbon (SOC), as the largest carbon pool on the land surface, plays an important role in soil quality, ecological security and the global carbon cycle. Multisource remote sensing data-driven modeling strategies are not well understood for accurately mapping soil organic carbon. Here, we hypothesized that the Sentinel-2 Multispectral Sensor Instrument (MSI) data-driven modeling strategy produced superior outcomes compared to modeling based on Landsat 8 Operational Land Imager (OLI) data due to the finer spatial and spectral resolutions of the Sentinel-2A MSI data. To test this hypothesis, the Ebinur Lake wetland in Xinjiang was selected as the study area. In this study, SOC estimation was carried out using Sentinel-2A and Landsat 8 data, combining climatic variables, topographic factors, index variables and Sentinel-1A data to construct a common variable model for Sentinel-2A data and Landsat 8 data, and a full variable model for Sentinel-2A data, respectively. We utilized ensemble learning algorithms to assess the prediction performance of modeling strategies, including random forest (RF), gradient boosted decision tree (GBDT) and extreme gradient boosting (XGBoost) algorithms. The results show that: (1) The Sentinel-2A model outperformed the Landsat 8 model in the prediction of SOC contents, and the Sentinel-2A full variable model under the XGBoost algorithm achieved the best results R2 = 0.804, RMSE = 1.771, RPIQ = 2.687). (2) The full variable model of Sentinel-2A with the addition of the red-edge band and red-edge index improved R2 by 6% and 3.2% over the common variable Landsat 8 and Sentinel-2A models, respectively. (3) In the SOC mapping of the Ebinur Lake wetland, the areas with higher SOC content were mainly concentrated in the oasis, while the mountainous and lakeside areas had lower SOC contents. Our results provide a program to monitor the sustainability of terrestrial ecosystems through a satellite perspective.

Thermal properties of Cambisols in mountain regions under different vegetation covers

Soil thermal properties regulate the thermal and water balance and influence the soil temperature distribution. The aim of the current study is  to  present data on the changes in the thermal properties of Cambisols at different ratios between the water content and the air in the pore space under different vegetation covers in mountain regions. The undisturbed soil samples were taken from the surface soil layers under grassland, deciduous and coniferous forests in three experimental stations of the Forest Research Institute – Gabra in Lozen Mountain, Govedartsi in Rila Mountain and Igralishte in Maleshevska Mountain. The soil thermal conductivity (λ), the thermal diffusivity (α) and the volumetric heat capacity (C&lt;sub&gt;v&lt;/sub&gt;) were measured with the SH-1 sensor of  a  KD2Pro device at different matric potentials in laboratory conditions. The thermal conductivity of the investigated soils was also measured with the TR-1 sensor of  a  KD2Pro device at the transitory soil moisture in field conditions. An increase in the thermal properties with the soil water content was best pronounced for λ and depended inversely on the total porosity. As the total porosity increased with the soil organic carbon content and decreased with the skeleton content, the lowest value of λ was established in the surface horizons of Dystric Cambisols (Humic) in the experimental station in Govedartsi. The soil thermal conductivity increased with the depth under the deciduous forest (Gabra and Igralishte) due to the lower soil organic carbon content (SOC) and the total porosity. There were no such changes in the subsurface horizon under the grassed associations. The increase in the heat capacity with the water content depended on the SOC to less extent. In the horizons with a SOC of less than 1.5%, the changes in the thermal diffusivity over the whole range of wetness were 1.7 times higher than those with a higher SOC.

Soil Chemical Properties and Fire Severity Assessment Using VNIR Proximal Spectroscopy in Fire-Affected Abandoned Orchard of Mediterranean Croatia

This paper aims to evaluate the ability of VNIR proximal soil spectroscopy to determine post-fire soil chemical properties and discriminate fire severity based on soil spectra. A total of 120 topsoil samples (0–3 cm) were taken from 6 ha of unburned (control (CON)) and burned areas (moderate fire severity (MS) and high fire severity (HS)) in Mediterranean Croatia within one year after the wildfire. Partial least squares regression (PLSR) and an artificial neural network (ANN) were used to build calibration models of soil pH, electrical conductivity (EC), CaCO3, plant-available phosphorus (P2O5) and potassium (K2O), soil organic carbon (SOC), exchangeable calcium (exCa), magnesium (exMg), potassium (exK), sodium (exNa), and cation exchange capacity (CEC), based on soil reflectance data. In terms of fire severity, CON samples exhibited higher average reflectance than MS and HS samples due to their lower SOC content. The PCA results pointed to the significance of the NIR part of the spectrum for extracting the variance in reflectance data and differentiation between the CON and burned area (MS and HS). DA generated 74.2% correctly classified soil spectral samples according to the fire severity. Both PLSR and ANN calibration techniques showed sensitivity to extract information from soil features based on hyperspectral reflectance, most successfully for the prediction of SOC, P2O5, exCa, exK, and CEC. This study confirms the usefulness of soil spectroscopy for fast screening and a better understanding of soil chemical properties in post-fire periods.

Soil organic carbon content and mineralization controlled by the composition, origin and molecular diversity of organic matter: A study in tropical alpine grasslands

The consensus for mechanisms controlling soil organic matter (SOM) persistence has shifted from traditional views based on SOM recalcitrance to a new paradigm based on SOM stabilization controlled by soil minerals and aggregates. Recent studies indicate that the origin, composition and molecular diversity of SOM are crucial to the decomposition and stabilization of SOM. However, it is not fully understood how the decomposition and stabilization of SOM are controlled at the molecular level. The objectives of this study were to investigate whether soil organic carbon (SOC) contents and mineralization are controlled by the composition, origin and molecular diversity of SOM. Soil samples were collected from contrasting bedrocks with different precipitation levels at tropical alpine grasslands of the Peruvian Andes. We applied a combination of a 76-day soil incubation experiment and pyrolysis-GC/MS assisted by thermochemolysis to investigate SOM decomposition and stabilization at the molecular level. The results indicated that soil samples with high SOC contents (92.6 ± 7.6 g kg−1 soil) and low SOC mineralization had abundant derivates of lignin, polysaccharides and n-alkanes. After the incubation, we observed neither a selective decomposition of any compound groups nor a decline of molecular diversity. In contrast, soil samples with low SOC contents (30.7 ± 2.8 g kg−1 soil) and higher SOC mineralization showed a depletion of plant-derived compounds, an accumulation of microbial-derived compounds and declined molecular diversity after the incubation. Furthermore, the SOC mineralization of these samples was positively correlated to the depletion of unsaturated fatty acids and the decrease in molecular diversity after the incubation. Therefore, we proposed that SOC contents and mineralization in our soils are (1) controlled by selective preservation of SOM molecular groups (e.g. plant-derived compounds), and (2) associated with changes in molecular diversity of SOM during microbial decomposition. Due to the selective preservation of organic compounds under different environmental conditions, we propose that environmental factors should be considered for the management of ecosystem services such as SOC sequestration in the studied region.

Improving the spatial prediction of soil organic carbon using environmental covariates selection: A comparison of a group of environmental covariates

In the digital soil mapping (DSM) framework, machine learning models quantify the relationship between soil observations and environmental covariates. Generally, the most commonly used covariates (MCC; e.g., topographic attributes and single-time remote sensing data, and legacy maps) were employed in DSM studies. Additionally, remote sensing time-series (RST) data can provide useful information for soil mapping. Therefore, the main aims of the study are to compare the MCC, the monthly Sentinel-2 time-series of vegetation indices dataset, and the combination of datasets (MCC + RST) for soil organic carbon (SOC) prediction in an arid agroecosystem in Iran. We used different machine learning algorithms, including random forest (RF), Cubist, support vector machine (SVM), and partial least square regression (PLSR). A total of 237 soil samples at 0–20 cm depths were collected. The 5-fold cross-validation technique was used to evaluate the modeling performance, and 50 bootstrap models were applied to quantify the prediction uncertainty. The results showed that the Cubist model performed the best with the MCC dataset (R2 = 0.35, RMSE = 0.26%) and the combined dataset of MCC and RST (R2 = 0.33, RMSE = 0.27%), while the RF model showed better results for the RST dataset (R2 = 0.10, RMSE = 0.31%). Soil properties could explain the SOC variation in MCC and combined datasets (66.35% and 50.82%, respectively), while NDVI was the most controlling factor in the RST (50.22%). Accordingly, results showed that time-series vegetation indices did not have enough potential to increase SOC prediction accuracy. However, the combination of MCC and RST datasets produced SOC spatial maps with lower uncertainty. Therefore, future studies are required to explicitly explain the efficiency of time-series remotely-sensed data and their interrelationship with environmental covariates to predict SOC in arid regions with low SOC content.