Quantification Of Soil Organic Carbon Research Articles

Assessing and mapping of soil organic carbon at multiple depths in the semi-arid Trans-Ural steppe zone

The quantification of soil organic carbon (SOC) and its vertical distribution is crucial for understanding carbon dynamics in terrestrial ecosystems. This study aimed to 2.5D digital mapping of SOC content in the Trans-Ural steppe zone (Russia) using a quantile regression forest (QRF) approach. The study utilized a dataset comprising 2495 SOC measurements collected from 1316 locations across three soil depths: 0–20 cm, 20–40 cm, and 40–60 cm. Environmental covariates were incorporated into the SOC modeling process, capturing major soil formation factors, and the uncertainty of the generated maps was estimated. The results revealed that SOC content ranged from 0.59 to 9.05 % in the topsoil, from 0.5 to 6.61 % in the subsurface layer and from 0.06 to 4.64 % in the subsoil. Based on the error metrics, including root mean square error (RMSE), coefficient of determination (R2), and Nash-Sutcliffe efficiency coefficient (NSE), we found a decrease in prediction accuracy with increasing soil depth. Furthermore, climate and vegetation variables, as well as elevation, emerged as key factors influencing the prediction of SOC concentrations at all depths. We also made an attempt to assess the future change of SOC under the influence of climate and anthropogenic impact. We anticipate that climate aridization and plowing will lead to a decline in SOC content in the Trans-Ural steppe region. Our findings contribute to the existing knowledge of SOC dynamics in steppe ecosystems at several depths, supporting informed decision-making for sustainable land use and climate change mitigation strategies.

Quantification of soil organic carbon: the challenge of biochar-induced spatial heterogeneity

Quantification of soil organic carbon: the challenge of biochar-induced spatial heterogeneity

Accurate Quantification of 0–30 cm Soil Organic Carbon in Croplands over the Continental United States Using Machine Learning

Increases in organic carbon within agricultural soils are widely recognized as a “negative emission” that removes CO2 from the atmosphere. Accurate quantification of soil organic carbon (SOC) to a certain depth in the spatial domain is critical for the effective implementation of improved land management practices in croplands. Currently, there is a lack of understanding regarding what depth strategy should be used to estimate SOC at 0–30 cm when sample datasets come from multiple depths. Furthermore, few studies have examined depth strategies for mapping SOC at the agricultural management level (i.e., field level), opting instead for point-based analysis. Here, three types of approaches with different depth strategies were evaluated for their ability to quantify 0–30 cm SOC content based on soil samples from 0–5 (surface), 5–30 (subsurface), and 0–30 cm (full column). These approaches involved the generalized additive model and machine learning techniques, i.e., artificial neural networks, random forest, and XGBoost. The soil samples used for the model evaluation and selection consisted of the newly collected samples in 2020–2022 and the Rapid Carbon Assessment (RaCA) legacy samples collected in 2010–2011. Environmental covariates corresponding to these SOC measurements were used in model training, including long-term physical climate, short-term weather, topographic and edaphic, and remotely sensed variables. Among the models evaluated in this study, the XGB regression model with a full column depth assignment strategy yielded the best prediction performance for 0–30 cm SOC content, with an r2 (squared Pearson correlation coefficient) of 0.48, an RMSE (root mean square error) of 0.29%, an ME (mean error) of 0.06%, an MAE of 0.25%, and an MEC (modeling efficiency coefficient) of 0.36 at the pixel level and an r2 of 0.64, an RMSE of 0.32%, an ME of −0.20%, an MAE of 0.28%, and an MEC of 0.48 at the field level. This study highlights that machine learning models with a full column depth strategy should be used to quantify 0–30 cm SOC content in agricultural soils over the continental United States (CONUS).

Aridity affects soil organic carbon concentration and chemical stability by different forest types and soil processes across Chinese natural forests

Forest soils play a critical role in carbon (C) reservoirs and climate change mitigation globally. Exploring the driving factors of soil organic carbon (SOC) concentration and stability in forests on a large spatial scale can help us evaluate the role of forest soils in regulating C sequestration. Based on SOC quantification and solid-state 13C nuclear magnetic resonance spectroscopy, we investigated the SOC concentration and SOC chemical stability (indicated by alkyl-to-O-alkyl ratio and hydrophobic-to-hydrophilic ratio) in top 0–5 and 5–10 cm soils from 65 Chinese natural forest sites and explored their driving factors. Results showed that SOC concentration in 0–5 cm soils were highest in mixed forests but SOC chemical stability in 0–5 cm soils were highest in coniferous forests, while SOC concentration and chemical stability in 5–10 cm soil layers did not differ across forest types. SOC concentration in 0–5 cm was directly related to soil pH and soil bacterial diversity. Structural equation models showed that aridity indirectly affected SOC concentration in 0–5 cm by directly affecting soil pH. While SOC chemical stability in 0–5 cm soils was higher with increased aridity. According to the correlations, the potential mechanisms could be attributed to higher proportion of coniferous forests in more arid forest sites, lower relative abundance of O-alkyl C, higher MgO and CaO contents, and higher bacterial diversity in soils from more arid forest sites. Our study reveals the important role of aridity in mediating SOC concentration and chemical stability in top 0–5 cm soils in Chinese natural forests on a large-scale field investigation. These results will help us better understand the different mechanisms underlying SOC concentration and stability in forests and assess the feedback of forest SOC to future climate change.

Remote Quantification of Soil Organic Carbon: Role of Topography in the Intra-Field Distribution

Soil organic carbon (SOC) measurements are an indicator of soil health and an important parameter for the study of land-atmosphere carbon fluxes. Field sampling provides precise measurements at the sample location but entails high costs and cannot provide detailed maps unless the sampling density is very high. Remote sensing offers the possibility to quantify SOC over large areas in a cost-effective way. As a result, numerous studies have sought to quantify SOC using Earth observation data with a focus on inter-field or regional distributions. This study took a different angle and aimed to map the spatial distribution of SOC at the intra-field scale, since this distribution provides important insights into the biophysiochemical processes involved in the retention of SOC. Instead of solely using spectral measurements to quantify SOC, topographic and spectral features act as predictor variables. The necessary data on study fields in South-East England was acquired through a detailed SOC sampling campaign, including a LiDAR survey flight. Multi-spectral Sentinel-2 data of the study fields were acquired for the exact day of the sampling campaign, and for an interval of 18 months before and after this date. Random Forest (RF) and Support Vector Regression (SVR) models were trained and tested on the spectral and topographical data of the fields to predict the observed SOC values. Five different sets of model predictors were assessed, by using independently and in combination, single and multidate spectral data, and topographical features for the SOC sampling points. Both, RF and SVR models performed best when trained on multi-temporal Sentinel-2 data together with topographic features, achieving validation root-mean-square errors (RMSEs) of 0.29% and 0.23% SOC, respectively. These RMSEs are competitive when compared with those found in the literature for similar models. The topographic wetness index (TWI) exhibited the highest permutation importance for virtually all models. Given that farming practices within each field are the same, this result suggests an important role of soil moisture in SOC retention. Contrary to findings in dryer climates or in studies encompassing larger areas, TWI was negatively related to SOC levels in the study fields, suggesting a different role of soil wetness in the SOC storage in climates characterized by excess rainfall and poorly drained soils.

Field-scale heterogeneity overrides management impacts following conversion to no-till within an arable system

Crop establishment in no-till arable systems benefits from soil health conducive for growth. Combined with the incorporation of crop residues and manures, no-till can influence soil organic carbon (SOC) and organic matter (SOM) dynamics, crop productivity and nutrient cycling. These processes are shaped by spatial and temporal factors including associated microbial activity. This study aimed to investigate the interaction between microbial and soil physicochemical properties during the transition from full-inversion to no-till soil management. Assessments were conducted over a two-year period and included a combination of soil microbial assays (microbial biomass carbon and nitrogen with physicochemical analyses, SOC, SOM, textural class, pH, gravimetric water content, and macronutrients). Two experiments were established within the same four-hectare field: one on a relatively level area (Experiment-1) and another on a slope (Experiment-2). Experiment-1 treatments consisted of Farmyard Manure (FYM and N-fertiliser), Green Manure (GM, Raphanus sativus and Vicia sp. mix) and Standard Practice (SP = Control, N-fertiliser only). Experiment-2 was a repeat of Experiment-1, but without the FYM treatment. Soil was sampled twice per crop season, in Spring and Autumn, in Experiment-1, and in Autumn only in Experiment-2. The results were influenced by spatial (i.e. where the same was collected from) and temporal (i.e. the time at which the same was collected) variations that were not always linked to management practices. This study demonstrated that the quantification of SOC and SOM were poor predictors of change in management practices over two years, while microbial biomass responded quickly to the incorporation of FYM. SOC and SOM were affected by soil texture, but not significantly by inputs, and were associated with extractable Ca2+ and total-N. Diachronic studies increase our understanding of biological and physicochemical dynamics in response to short-term change in soil management practices. This study emphasises the impact of soil texture within a single heterogenous field, and how it affects management outcomes. It highlights the importance of considering spatial differences to develop effective and sustainable agricultural solutions.

Predicting soil organic carbon in cultivated land across geographical and spatial scales: Integrating Sentinel-2A and laboratory Vis-NIR spectra

Digital mapping of soil organic carbon (SOC) is essential for visualizing the spatial distribution at different regions and scales. However, existing studies using remote sensing are limited by the low spectral resolution of multispectral data for accurate estimation of SOC, and the failure of laboratory visible and near-infrared and shortwave infrared (Vis-NIR) spectroscopy to perform pixel-level mapping. To address these limitations, this study proposes a SOC mapping framework that integrates satellite earth observations and proximal sensing spectral data to reconstruct images having a spectral resolution of 400–2500 nm and a spatial resolution of 10 m, then the clustering probability analysis was applied on the reconstructed images, to develop a high accuracy SOC prediction model and demonstrate its effectiveness across geographical and spatial scales. Specifically, a total of 324 topsoil samples were collected from Baoqing and Suiling counties in northeast China, whose laboratory Vis-NIR reflectance spectra and SOC were measured. Sentinel-2A images of bare soils averaged over three years before and after the samples collection (i.e., 2018 to 2021) were collected. A total of 264 samples from eight fields in Belgium were used to evaluate the performance of the model at the field scale. Then, two models were developed (i) a global regression model with direct prediction using random forest and (ii) a clustering probability model with Gaussian Mixture Model-random forest. Results demonstrated that (1) reconstructed images produced more accurate prediction results than the individual laboratory Vis-NIR spectroscopy and Sentinel-2A models. (2) At the regional scale, clustering probability model achieved better performance in quantification of SOC (coefficient of determination [R2] = 0.78, and root mean square error [RMSE] = 0.78 %) than global regression model (R2 = 0.65, and RMSE = 1.06 %). (3) For field scale, the reconstructed image-based clustering probability SOC prediction model was shown to be effective when using the Belgium data, providing a R2 of 0.67, and RMSE of 0.15 %. (4) Integrating remote sensing multispectral and proximal Vis-NIR information can lead to a smoother and more continuous SOC distribution within the region, and can better show its spatial heterogeneity within the field. This study highlights the advantages of integrating remote sensing with proximal sensing for improving the accuracy of SOC prediction and mapping, enabling applicability of the model across geographical and spatial scales with appreciable accuracy.

Quantifying organic carbon in particulate and mineral-associated fractions of calcareous soils – A method comparison

Accurate quantification of soil organic carbon (OC) pools is essential to study the dynamics of OC in soils. Therefore, organic matter is often separated into physical fractions with distinct turnover times, e.g. particulate (POM) and mineral-associated organic matter (MAOM), after which their OC content is measured. Calcareous soils are under-represented in such fractionation studies, because the required analytical differentiation between OC and inorganic carbon (IC) is not straightforward and implies more processing time, sample mass and equipment. Here, we review the performance of four methods to quantify OC in POM and MAOM fractions of soils containing 3–16 g IC kg−1 and 10–25 g OC kg−1 (n = 16). We assessed the similarity and consistency of the data obtained with the different methods. Furthermore, we checked how their ability to distinguish OC from IC was influenced by the fractionation and in particular by the dispersion with sodium hexametaphosphate (SHMP). The four methods were: 1) Elemental carbon analysis after removal of IC by acid fumigation (OCfum); 2) Elemental carbon analysis after removal of IC with aqueous acid (OCaq); 3) Elemental carbon as determined by Rock-Eval 6 thermal analysis (OCRE6); and 4) Elemental carbon analysis after removal of OC by loss on ignition and subsequent quantification of OC by difference between total carbon and IC (OCLOI). We found that the OCfum, OCaq, and OCLOI methods produced similar OC contents for bulk soils, whereas OCRE6 results were slightly but significantly lower. Total recovered OCRE6 contents of the summed POM and MAOM fractions were similar to the bulk soil OCRE6 contents. In contrast, the recovered OCfum, OCaq and OCLOI contents were slightly higher than respective bulk soil OC contents (108–112%), especially in soils with high IC/OC ratios. We show that this was not caused by operational artefacts or chemical changes that occurred during OC fractionation, but rather likely indicated error propagation. We conclude that all 4 methods can reliably quantify OC in POM and MAOM fractions in calcareous soils and that the optimal choice depends on the required accuracy of the results and the available sample mass, lab equipment and processing time. Based on our findings and practical considerations, we provide guidance for the selection of the most optimal OC quantification method to study OC dynamics in calcareous soils.

Evolution of Maize Compost in a Mediterranean Agricultural Soil: Implications for Carbon Sequestration

Compost amendments, apart from improving fertility and the general characteristics of agricultural soils, have known implications for global C cycling and sequestration in soils. Their effects are usually assessed via the quantification of soil organic carbon (SOC) pools, usually labile (fast) and recalcitrant (slow) pools, with varying intrinsic decomposition rates and distinct resident times. However, the real C-sequestration potential of organic additions to soil is still under discussion. In this study, a field trial and a lab incubation experiment were designed to study the C-sequestration mechanism in an agricultural Mediterranean soil. Soil with a history of C3 photosystem crop was amended with two maize composts from maize harvesting surpluses (C4 photosystem) with different maturity stages (AC: aged compost; NC: new, less mature compost). The evolution of SOM was monitored for 6 months using complementary analytical techniques, including analysis of stable C isotopes (IRMS), thermogravimetry (TG) and C-stock and priming effect (PE) modelling. Based on the natural C-isotope labelling, the proportion of new C was calculated. More than 50% of the C added to the soil with the compost was incorporated into the SOM in only 6 months. However, the application of maize compost did not always enhance soil C-sequestration capacity. The addition of compost caused a general PE, enhancing SOM decay and reducing the fast (labile) SOM mean residence time (MRT) (11.2 days). This was more pronounced with the addition of a higher dose of AC, causing a PE up to a 718%. On the other hand, a higher MRT (54.4 days) occurred in soils with NC applied, likely due to its deleterious effects, limiting heterotrophic activity. Despite that, the average MRT of the slow (recalcitrant) SOM pool was lower than usually reported. The application of higher doses of both composts generally showed greater MRT values compared to control (1.7 years vs. 3.8 and 2.9 years for NC and AC, respectively), leading to an increase in this more stable C pool and effective soil C sequestration. The results described in this work may help readers to better understand SOM dynamics and may be of use in designing appropriate management strategies for improving OM quantity and quality and to optimize C storage in Mediterranean soils.

Long-term tillage and irrigation effects on aggregation and soil organic carbon stabilization mechanisms

Sustainable soil management practices are required in agriculture to enhance carbon sequestration and restore soil functions. Here, the aim was to investigate the effect of different tillage practices combined with or without irrigation on (i) soil organic carbon (SOC) content, (ii) fungal biomass and their relationships with aggregate size classes in the soil surface layer; further, (iii) the concept of soil particle saturation with SOC was tested to evaluate if a threshold was reached in a 14 year-experiment. Our hypothesis was that long-term irrigation, intensive tillage and their combination, would negatively affect soil aggregation and SOC stabilization. The experiment has started in 2003 on a research farm in Canterbury, New Zealand. The present work focused on two contrasting tillage practices –intensive tillage with 20–25 cm ploughing (IT) and direct drill (DD)– combined with sprinkler-irrigated and non-irrigated (hereafter called Rainfed) conditions in a split-plot experimental design. Soil samples (0–5 cm layer) were analyzed for pore size distribution, specific surface area and microbial biomass. Further, wet sieving was used to isolate large macroaggregates (LM, > 2000 μm), small macroaggregates (SM, 250–2000 μm), microaggregates (m, 53–250 μm), particle sized silt + clay fractions (s+c, < 53 μm) and Fine20 particles (<20 μm), followed by the analysis of aggregate morphology and SOC quantification in them. Results showed that both DD and Rainfed management increased total SOC content of the bulk soil. Only the LM fraction and the SOC therein (OC-LM) increased significantly in DD compared to IT, while m and s+c fractions and OC-m and OC-s+c did not differ between treatments. Macroaggregate breakdown processes and measured SOC therein had likely not reached steady-state conditions, as suggested by the lack of any SOC differences in the aggregate size classes < 250 µm. In contrast, the Fines20:SOC ratio differentiated between soils that had reached (i.e., DD) or not reached (i.e., IT) the saturation threshold. Finally, it was observed that a higher fungal:bacteria (F:B) ratio was generally accompanied by a greater LM fraction and mean weight aggregate diameter, highlighting the importance of fungi in the formation of LM. These results suggested that our hypothesis of detrimental effects on soil aggregation and SOC accumulation of both tillage and irrigation was not fully demonstrated yet. A longer study period would be required to better understand the effects of such practices of SOC storage.

Prediction of the soil organic carbon in the LUCAS soil database based on spectral clustering

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

Replicating measured site-scale soil organic carbon dynamics in the U.S. Corn Belt using the SWAT-C model

Accurate quantification of soil organic carbon (SOC) change is essential for designing effective agricultural practices to maximize agronomic, climatic, and environmental benefits. Here, we modified the SOC algorithms within the Soil and Water Assessment Tool – Carbon (SWAT-C) model and applied it to simulate SOC dynamics across seven sites in the U.S. Corn Belt. We examined multiple methods for estimating the effects of soil temperature (3 options), soil water (2 options), and tillage (4 options) on SOC decomposition. We found that model simulation results are sensitive to the choice of methods and identified the best performing combination of methods. Further analyses show that the model captured well SOC dynamics at different sites and soil depths and under different tillage intensities. SWAT-C, as an open-source model, is shared to contribute to future carbon assessment and management in agroecosystems and support robust decision making to promote climate smart agriculture.

Microspectroscopic visualization of how biochar lifts the soil organic carbon ceiling

The soil carbon (C) saturation concept suggests an upper limit to the storage of soil organic carbon (SOC). It is set by the mechanisms that protect soil organic matter from mineralization. Biochar has the capacity to protect new C, including rhizodeposits and microbial necromass. However, the decadal-scale mechanisms by which biochar influences the molecular diversity, spatial heterogeneity, and temporal changes in SOC persistence, remain unresolved. Here we show that the soil C storage ceiling of a Ferralsol under subtropical pasture was raised by a second application of Eucalyptus saligna biochar 8.2 years after the first application—the first application raised the soil C storage ceiling by 9.3 Mg new C ha−1 and the second application raised this by another 2.3 Mg new C ha−1. Linking direct visual evidence from one-, two-, and three-dimensional analyses with SOC quantification, we found high spatial heterogeneity of C functional groups that resulted in the retention of rhizodeposits and microbial necromass in microaggregates (53–250 µm) and the mineral fraction (<53 µm). Microbial C-use efficiency was concomitantly increased by lowering specific enzyme activities, contributing to the decreased mineralization of native SOC by 18%. We suggest that the SOC ceiling can be lifted using biochar in (sub)tropical grasslands globally.

Using soil library hyperspectral reflectance and machine learning to predict soil organic carbon: Assessing potential of airborne and spaceborne optical soil sensing

Soil organic carbon (SOC) is a key variable to determine soil functioning, ecosystem services, and global carbon cycles. Spectroscopy, particularly optical hyperspectral reflectance coupled with machine learning, can provide rapid, efficient, and cost-effective quantification of SOC. However, how to exploit soil hyperspectral reflectance to predict SOC concentration, and the potential performance of airborne and satellite data for predicting surface SOC at large scales remain relatively underknown. This study utilized a continental-scale soil laboratory spectral library (37,540 full-pedon 350–2500 nm reflectance spectra with SOC concentration of 0–780 g·kg−1 across the US) to thoroughly evaluate seven machine learning algorithms including Partial-Least Squares Regression (PLSR), Random Forest (RF), K-Nearest Neighbors (KNN), Ridge, Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) along with four preprocessed spectra, i.e. original, vector normalization, continuum removal, and first-order derivative, to quantify SOC concentration. Furthermore, by using the coupled soil-vegetation-atmosphere radiative transfer model, we simulated twelve airborne and spaceborne hyper/multi-spectral remote sensing data from surface bare soil laboratory spectra to evaluate their potential for estimating SOC concentration of surface bare soils. Results show that LSTM achieved best predictive performance of quantifying SOC concentration for the whole data sets (R2 = 0.96, RMSE = 30.81 g·kg−1), mineral soils (SOC ≤ 120 g·kg−1, R2 = 0.71, RMSE = 10.60 g·kg−1), and organic soils (SOC > 120 g·kg−1, R2 = 0.78, RMSE = 62.31 g·kg−1). Spectral data preprocessing, particularly the first-order derivative, improved the performance of PLSR, RF, Ridge, KNN, and ANN, but not LSTM or CNN. We found that the SOC models of mineral and organic soils should be distinguished given their distinct spectral signatures. Finally, we identified that the shortwave infrared is vital for airborne and spaceborne hyperspectral sensors to monitor surface SOC. This study highlights the high accuracy of LSTM with hyperspectral/multispectral data to mitigate a certain level of noise (soil moisture <0.4 m3·m−3, green leaf area < 0.3 m2·m−2, plant residue <0.4 m2·m−2) for quantifying surface SOC concentration. Forthcoming satellite hyperspectral missions like Surface Biology and Geology (SBG) have a high potential for future global soil carbon monitoring, while high-resolution satellite multispectral fusion data can be an alternative.

Quantification of soil organic carbon at regional scale: Benefits of fusing vis-NIR and MIR diffuse reflectance data are greater for in situ than for laboratory-based modelling approaches

Benefits of fusion approaches for visible to near (vis-NIR) and mid-infrared (MIR) chemometric modelling have been studied to some extent for laboratory-based soil studies, but little is known about the usefulness and limitations for in situ studies. Objectives were to compare laboratory-based and in situ vis-NIR and MIR partial least squares (PLS) and bagging-PLS regression approaches and to explore the potentials of combining both types of spectral data for the quantification of soil organic carbon (SOC). We applied different established low-level (spectra concatenation, outer product fusion approach) and high-level (averaging of vis-NIR and MIR modelling results) data fusion methods. The studied set comprised a total of 186 soil samples collected in Saxony-Anhalt and northern Saxony, Central Germany. One subset (Querfurt Plateau) covered 90 finely-textured soils originating from the Chernozem soil region, another (Düben Heath) with 96 samples was characterized by a wider pedological variety. Vis-NIR and MIR diffuse reflectance spectra were measured in situ on the soil surface and in the laboratory on pre-treated (dried and finely ground) soil material with the ASDFieldSpec4 and the Agilent 4300 Handheld FTIR instruments. We found a regionally stratified approach to be beneficial for accurate estimations for both laboratory and in situ data. For laboratory spectra, MIR outperformed vis-NIR data in both regions (Querfurt Plateau: r2 = 0.85 vs. 0.65, RMSE = 0.11 (in % SOC) vs. 0.17; Düben Heath: 0.77 vs. 0.69 (r2) and 0.27 vs. 0.40 (RMSE)). Ranking for in situ data was the same, but accuracies decreased markedly. With MIR, r2 amounted to 0.58 and RMSE was 0.20 for the Querfurt Plateau (vis-NIR: r2 = 0.26, RMSE = 0.27); for Düben Heath, r2 was 0.60 and RMSE was 0.39 for MIR data, while vis-NIR resulted in an r2 of 0.53 and an RMSE of 0.43. For the studied samples, which had medium to low water contents (0.68 to 16.8 wt%, median at 5.4 wt%), we found accuracies with both spectral datasets to be similarly affected by in situ conditions. Model ensemble averaging based on bagging-PLS regression was the most efficient approach to improve SOC estimation accuracies with in situ spectral data, whereas model averaging was in general of little effect for laboratory data. Improvements were most marked for the in situ data of the Düben Heath region, where r2 increased to a value of 0.77 and RMSE decreased to 0.28. Low-level data fusion methods did not yield any improvements compared to model ensemble averaging. For the latter, we identified averaging with weights derived sample-wise from uncertainties in the bootstrap-based modelling as being most accurate, but with little benefits compared to a simple (unweighted) averaging of vis-NIR and MIR estimates. Our results suggest that already a simple averaging procedure has the potential to advance multi-sensor applications integrating vis-NIR and MIR data for in situ or on-site soil spectroscopy. This applies especially to regions with heterogeneous soil conditions, tied to spectral variablity, as this increases the probability of complementary vis-NIR and MIR information and their prospective fusion.

Water-absorption-trough dewatering machine for estimation of organic carbon in moist soil

Quantitative estimation of soil organic carbon (SOC) is essential for the study of the C cycle and global C storage. Soil spectroscopic technology provides a cost-effective and time-efficient method for SOC quantification and has been successfully used to determine SOC storage. However, the SOC estimation accuracy remains limited by other soil properties, particularly soil water. In this study, we proposed a new deep learning algorithm named the Water Absorption Trough Dewatering Machine (WATDM) to improve estimations of SOC from soil reflectance spectra and reduce the effect of soil water. Soil water and reflectance spectral data of soil samples were measured using spectrometry. Based on the soil water contents derived from the water absorption troughs around 1900 nm, the optimal WATDM model was obtained and treated as the final model of the WATDM method, which performed better than a multiple linear regression model based on moist soil samples. The findings of this study indicate that the WATDM method can improve the estimation accuracy of SOC content by reducing the effect of soil water and can be used as a valuable new methodology within the spectroscopic estimation of soil properties.

Using Diffuse Reflectance Spectroscopy as a High Throughput Method for Quantifying Soil C and N and Their Distribution in Particulate and Mineral-Associated Organic Matter Fractions

Large-scale quantification of soil organic carbon (C) and nitrogen (N) stocks and their distribution between particulate (POM) and mineral-associated (MAOM) organic matter is deemed necessary to develop land management strategies to mitigate climate change and sustain food production. To this end, diffuse reflectance mid-infrared spectroscopy (MIR) coupled with partial least square (PLS) analysis has been proposed as a promising method because of its low labor and cost, high throughput and the potential to estimate multiple soil attributes. In this paper, we applied MIR spectroscopy to predict C and N content in bulk soils, and in POM and MAOM, as well as soil properties influencing soil C storage. A heterogeneous dataset including 349 topsoil samples were collected under different soil types, land use and climate conditions across the European Union and the United Kingdom. The samples were analyzed for various soil properties to determine the feasibility of developing MIR-based predictive calibrations. We obtained accurate predictions for total soil C and N content, MAOM C and N content, pH, clay, and sand (R2&amp;gt; 0.7; RPD&amp;gt;1.8). In contrast, POM C and N content were predicted with lower accuracies due to non-linear dependencies, suggesting the need for additional calibration across similar soils. Furthermore, the information provided by MIR spectroscopy was able to differentiate spectral bands and patterns across different C pools. The strength of the correlation between C pools, minerals, and C functional groups was land use-dependent, suggesting that the use of this approach for long-term soil C monitoring programs should use land-use specific calibrations.

Estimation of the carbon valence from its average formal oxidation state in the soil organic matter

AbstractThe wet dichromate methods for the quantification of soil organic carbon on the basis of the reductant ability of soil organic matter have been widely used and they are still nowadays. The results obtained with these methods depart from the reference dry combustion with CO2 measurement and the discrepancies are interpreted in terms of incomplete oxidation under the wet dichromate conditions. However, in the calculations accompanying the dichromate methods a carbon valence (VC) equal to four is systematically used, even though the actual VC could be different. To assess the importance that an actual VC different from four could have in the mismatch between the wet dichromate methods and the reference dry combustion, the relationship between VC and the average formal oxidation state of carbon (zC) in soil organic matter should be known. On the basis of the definition of the equivalent weight and the average formal oxidation state, it is shown that a simple linear expression of the form VC = 4 – zC relates both concepts in carbon‐hydrogen‐oxygen‐nitrogen‐phosphorus‐sulphur compounds, which are representative of soil organic matter. On the basis of this equation the actual VC in soil organic matter can be assessed, thus helping in the interpretation of soil organic carbon determinations with the wet dichromate methods and, therefore, in the comparison between the dichromate methods and the reference dry combustion.Highlights Although questioned, a carbon valence of four is always used in the wet dichromate methods for SOC. How the valence of carbon depends on its average formal oxidation had not been previously shown. Both concepts are related through a simple linear equation: VC = 4 – zC. The mismatch between the dichromate oxidation and the dry combustion will be better interpreted.

Interaction between edaphic mesofauna and organic carbon within water-stable aggregates in forestry systems: A case study in northeastern Brazil

Soil mesofauna plays an important role in soil aggregation, which conditions the physical protection of soil organic carbon (SOC). Thus, our study associates the evaluation of mesofauna and SOC quantification to generate relevant information on the influencing level of these organisms on organic matter dynamics in aggregates. It is even more important investigating these indicators in places where decomposition processes are more intense, such as forest systems under tropical soils, where little is known about the interaction between these variables. In this context, the present study aimed to evaluate: (1) the influence of homogeneous Pterogyne nitens and Pinus caribaea plantations on the edaphic mesofauna community and on SOC stocks and carbon in soil aggregate classes using a native forest as a reference; and (2) the interaction between edaphic mesofauna, SOC and aggregate carbon. This study was performed on Oxisol soil areas located in the municipality of Vitória da Conquista, Bahia, Brazil. The edaphic mesofauna was evaluated using the modified Berlese-Tullgren funnel method. Physical soil fractionation was carried out by wet sieving. The organic carbon contents of the soil and aggregates were determined by wet oxidation. Implantation of homogeneous Pterogyne nitens and Pinus caribaea stands causes a reduction in the edaphic mesofauna (abundance and average richness) and in the amount of soil macroaggregates. The differences between the forest systems are largely explained by the abundance of fauna, SOC and carbon of the macroaggregates, microaggregates and silt + clay. The edaphic mesofauna has a direct and positive influence on the SOC, which highlights its important role in regulating the soil carbon dynamics in the soil.

Application of VIS-NIR spectroscopy for estimation of soil organic carbon using different spectral preprocessing techniques and multivariate methods in the middle Indo-Gangetic plains of India

Soil organic carbon (SOC) is the main source of soil nutrients, which are essential for the growth and development of agricultural crops. Precise and quick quantification of SOC is of utmost importance in crop husbandry and soil health/carbon sequestration quantification. In order to evaluate visible and near-infrared diffuse reflectance spectroscopy (VIS-NIR) as an alternative to precise and quick method of quantification of SOC in the Indo-Gangetic plains, 280 soil samples were collected covering Inceptisols, Entisols and Alfisols and their spectra recorded. Six preprocessing techniques ((reflectance, absorbance, multiplicative scatter correction (MSC), standard normal variate (SNV), Savitzky–Golay smoothing first derivative (SG-FD) and Savitzky–Golay smoothing second derivative (SG-SD)) and four multivariate methods (partial least-squares regression (PLSR), random forest (RF), support vector regression (SVR) and multivariate adaptive regression splines (MARS)) were evaluated to predict SOC from VIS-NIR spectra. The considerable prediction accuracy and robustness were achieved using the PLSR model (RV2 = 0.73, RMSEV = 0.07, and RPDV = 1.90), RF model (RV2 = 0.69, RMSEV = 0.07, and RPDV = 1.74), SVR model (RV2 = 0.57, RMSEV = 0.08), and RPDV = 1.50), and MARS model (RV2 = 0.63, RMSEV = 0.10, and RPDV = 1.05). Findings from this study identified the reliability of SOC determinations by examining how preprocessing techniques and multivariate methods affect spectral analyses.