Variability Of Soil Organic Matter Research Articles

Scale-Location Dependence Relationship between Soil Organic Matter and Environmental Factors by Anisotropy Analysis and Multiple Wavelet Coherence

Soil organic matter (SOM) and environmental factors have been shown to have a scale-location dependence relationship. However, few studies have considered the anisotropy, and the scale-location dependence relationship may not be fully characterized. In this study, transects with dominant directions of SOM variability in the dryland farming regions of Songliao Plain, China were extracted by anisotropy analysis. The scale-location specific multivariate relationships between SOM and environmental factors along the two transects were examined using multiple wavelet coherence. Results indicated that the scale and location-specific variations in SOM and environmental factors were direction-specific. The major direction with the most significant SOM variations was 56° east by north, while the minor direction was perpendicular to the major direction. The strongest single factor for explaining SOM variations differed between two dominant directions, sand along the major direction (average wavelet coherence (AWC) = 0.57, percentage area of significant coherence (PASC) = 40.32% at all scales) and bulk density (BD) along the minor direction (AWC = 0.66, PASC = 50.16% at all scales). The combination of mean annual temperature (MAT) and BD was the best to explain SOM variations along the major direction (AWC = 0.78, PASC = 46.23% at all scales). A two-factor combination is adequate to explain SOM variability along the major direction, whereas a single factor is sufficient for the explanation along the minor direction. More factors did not evidently increase or even decrease the percentage of scale-location domains where SOM variations were significantly explained. This work has important implications for developing future sampling strategies and preparing detailed digital soil maps.

Impacts of Rice-Rape Rotation on Major Soil Quality Indicators of Soil in the Karst Region.

Arable land resources in karst regions are relatively scarce. The original crop rotation pattern can no longer meet the requirements of productivity development, while different crop rotation patterns have different impacts on the physicochemical properties of the soil. Through field experiments and laboratory analysis, the physicochemical properties and pollution characteristics of the soil during different crop growing stages in rice–rape rotation were investigated systematically. The main results are as follows. During the rice–rape rotation, fine sand in the topsoil experienced the greatest variation. During the rotation, pH variation in the subsoil was greater than that in the topsoil. The soil in paddy fields was poorly ventilated, and the rotation could reduce the redox potential of the soil. In the rotation process, the soil organic matter in the topsoil was higher than that in the subsoil, but the variation of soil organic matter in the topsoil was lower than that in the subsoil. The worst Cd pollution of the topsoil occurred in the seedling stage of rice, while that of the subsoil occurred in the flowering stage of rape; the comprehensive pollution index of Cr and Cd in the subsoil was higher than that in the topsoil. It is of great significance to investigate efficient crop rotation patterns under the conditions of the current productivity for promoting sustainable increases of rape and rice yield, maintaining soil fertility, and improving the soil.

A global synthesis of patterns in soil organic matter and temperature sensitivity along the altitudinal gradient

Although it is important to determine how the global carbon (C) cycle is responding to climate change in our three-dimensional Earth, variation in the soil organic matter (SOM) and temperature sensitivity (Q10) of soil respiration along the altitudinal gradient remains unclear globally. This study explored spatial variability in SOM and Q10 and its regulatory mechanisms from a three-dimensional perspective. We collected SOM and Q10 datasets from published studies for 0–10 cm soil depth along elevational gradients of mountain ranges globally. We found that the SOM content in most mountains changed significantly with increasing altitude; unexpectedly, these changes with altitude significantly differed across different climate zones. Specifically, the SOM content increased with increasing altitude in tropical and subtropical regions, while it decreased with increasing altitude in temperate and plateau regions. Climate factors (mean annual temperature and mean annual precipitation) and soil properties (total N content, C/N ratio, and pH) together accounted for 83% of spatial variation in SOM with altitude. Q10 values, under both laboratory incubation and in situ studies, did not significantly correlate with altitude, and no apparent global patterns were detected (mean: 2.07); however, Q10 was regulated by variation in soil substrate quality along altitudinal gradients, to some extent. In the context of global climate change, this new information on SOM and Q10 in relation to the altitude at a global scale might enhance our understanding of soil C−climate feedbacks.

Examining the Driving Factors of SOM Using a Multi-Scale GWR Model Augmented by Geo-Detector and GWPCA Analysis

A model incorporating geo-detector analysis and geographically weighted principal component analysis into Multi-scale Geographically Weighted regression (GWPCA-MGWR) was developed to reveal the factors driving spatial variation in soil organic matter (SOM). The regression accuracy and residuals from GWPCA-MGWR were compared to those of the classical Geographically Weighted regression (GWR), Multi-scale Geographically Weighted regression (MGWR), and GWPCA-GWR. Our results revealed that local multi-collinearity on model fitting negatively affects the results to different degrees. Additionally, compared to other models, GWPCA-MGWR provided the lowest MAE (0.001) and little-to-no residual spatial autocorrelation and is the best model for regression for SOM spatial distribution and identification of dominant driving factors. GWPCA-MGWR produced spatial non-stationary SOM that was variably affected by soil nutrient content, soil type, and human activity, and was geomorphic in the second place. In conclusion, the spatial information obtained from GWPCA-MGWR provides a valuable reference for understanding the factors that influence SOM variation.

Determining Optimal Sampling Numbers to Investigate the Soil Organic Matter in a Typical County of the Yellow River Delta, China

Soil organic matter (SOM) plays a crucial role in promoting soil tillage, improving soil fertility and providing crop nutrients. Investigation and sampling are the premise and basis for understanding the spatial distribution of SOM. The number of sampling points will affect the accuracy of spatial variation of SOM. Therefore, it is important scientific work to determine a reasonable number of sampling points under the premise of ensuring accuracy. In this study, Kenli County, a typical area of the Yellow River Delta in China, was taken as an example to investigate the effect of different sampling points on spatial-variation expression of SOM. A total of 12 sample subsets (including 900 samples) were randomly sampled at equal intervals from the 900 sample points, using geographic information system (GIS) technology and geostatistical analyses to explore the optimal number of samples. The results showed that the SOM content in the study area had a lower-middle degree of variation. As the number of sample points decreased, the spatial distribution of SOM showed the gradual weakening of detail-characterization ability; and when the number of sample points was too small (<100), there was a wrong expression that was not consistent with the actual situation. The value of RMSE has no obvious regularity with the change of sample number. The values of both ME and ASE showed a significant inflection point when the number of samples was 150 and remained around 0 and 4 as the number of samples increased, respectively. Combined with the three indicators of ME, RMSE and ASE, collecting at least 150 samples can satisfy the spatial-variation expression of SOM, equivalent to 107 sample points within the area of 1000 km2. The research results could provide important references for investigation of SOM content in areas with similar natural geographical conditions.

Spatial Variability of Soil Properties in Palm Groves of the Central Algerian Sahara (Case of Zelfana)

Monitoring soil quality in irrigated areas is essential for assessing the sustainability of production systems. In this respect, the spatial variability of the properties of irrigated soils is a mean to know the evolution of the latter. This study aims to determine the spatial variability of soil organic matter, salinity, pH and active limestone using a geostatistical approach. The present study was carried out in the region of Zelfana located in the Algerian central Sahara, the samples were collected from a depth of 0 to 30 cm and analysed for organic matter, salinity, pH and active limestone. The analytical results show that the soil is very poor in organic matter, very salty, alkaline to very alkaline and moderately calcareous. The geostatistical analysis revealed various patterns and levels of spatial distribution of the studied properties. The results showed a weak spatial dependence for organic matter, moderate for pH and salinity and strong for active limestone. The variographic analysis showed that the nugget effect is weak for organic matter and pH, moderate for salinity, while active limestone does not show a nugget effect. The range varies from 75 meters for salinity to 299 meters for organic matter, confirming the validity of the adopted sampling and allowing the optimization of future sampling plans. The Arcgis autoKriging function was used to select the best theoretical variogram model from those most commonly used in geostatistics (Gaussian, spherical, exponential and circular). This model was used to produce the spatial variability maps using ordinary kriging. Spatial variability of soil properties is influenced by agricultural intensification, something that must be taken into consideration for integrated and sustainable land management in similar regions.

Status of Essential Elements in Soil and Grain of Organically Produced Maize, Spelt, and Soybean

Organic agriculture offers many benefits through the increased nutritional quality of produced crops, agro-ecosystem preservation, and climate change mitigation. The development of an efficient nutrient management strategy in low-input systems, such as organic agriculture, which supports soil fertility and essential nutrients absorption by crops, is continually exploring. Thus, a study with maize–spelt–soybean rotation during a 5-year period in organic production was established to evaluate the variability in soil organic matter (SOM) and the status of available elements: N, P, K, Ca, Mg, Fe, Cu, Mn, Zn, and Si from the soil, as well as grain yield (GY) and the content of protein, P, K, Ca, Mg, Fe, Cu, Mn, Zn, and Si concentration in the grain of spelt, maize, and soybean. Significant variations in mineral elements in the soil, GY, and grain composition were detected. Spelt achieved the highest average GY, while soybean grain was the richest in a majority of examined nutrients. The soil Ca content was important for GY, while the protein level in grain was generally tied to the Mn level in the soil. It was recognized that soil–crop crosstalk is an important strategy for macro- and micro-nutrients management in the soil and grain of organically produced spelt, maize, and soybean. While a reduction in the GY and protein concentration in grain was present over time, it was established that a low-input system under dry-farming conditions supports nutrient availability and accumulation in grain, under semi-arid agro-ecological conditions of central Serbia.

Source Apportionment of Speciated Mercury in Chinese Rice Grain Using a High-Resolution Model.

Rice grain consumption is a primary pathway of human mercury exposure. To trace the source of rice grain mercury in China, we developed a rice paddy mercury transport and transformation model with a grid resolution of 1 km × 1 km by using the unit cell mass conservation method. The simulated total mercury (THg) and methylmercury (MeHg) concentrations in Chinese rice grain ranged from 0.08 to 243.6 and 0.03 to 238.6 μg/kg, respectively, in 2017. Approximately, 81.3% of the national average rice grain THg concentration was due to atmospheric mercury deposition. However, soil heterogeneity, especially the variation in soil mercury, led to the wide rice grain THg distribution across grids. Approximately, 64.8% of the national average rice grain MeHg concentration was due to soil mercury. In situ methylation was the main pathway via which the rice grain MeHg concentration was increased. The coupled impact of high mercury input and methylation potential led to extremely high rice grain MeHg in partial grids among Guizhou province and junctions with surrounding provinces. The spatial variation in soil organic matter significantly impacted the methylation potential among grids, especially in Northeast China. Based on the high-resolution rice grain THg concentration, we identified 0.72% of grids as heavily polluted THg grids (rice grain THg > 20 μg/kg). These grids mainly corresponded to areas in which the human activities of nonferrous metal smelting, cement clinker production, and mercury and other metal mining were conducted. Thus, we recommended measures that are targeted at the control of heavy pollution of rice grain by THg according to the pollution sources. In addition, we observed a wide spatial variation range of MeHg to THg ratios not only in China but also in other regions of the world, which highlights the potential risk of rice intake.

Temporal dynamics of soil nutrients in the riparian zone: Effects of water fluctuations after construction of the Three Gorges Dam

The Three Gorges Dam (TGD) plays a vital role in the hydropower supply of China, which has a substantial impact on the riparian zones along the Yangtze River. Knowledge about how riparian soil properties respond to such changes remains limited. To explore how water regulation disturbs the status of soil nutrients in the riparian zone, a long-term monitoring program was implemented at sites with secondary shrubland (SB) and abandoned farmland (AFD). A total of 288 composite soil samples collected from the two riparian areas were investigated. Compared with 2009, long-term flooding changed the distribution of soil organic matter (SOM) and available phosphorus (AP) contents in various soil layers. The fluctuation of the water level reduced the SOM content, which the range of SOM variation was greater in the early stage of submergence. In 2018, the SOM content in the 10–20 cm at 155–165 m above sea level (a.s.l.) of the AFD was 9.46 g/kg, which reduced by 74.30% compared with 2019. In all the 0–10 cm soil layer, except for 145–155 (a.s.l.) in the AFD plot, soil total nitrogen contents in 2018 were significantly higher than those in 2009. Compared with 2009, the soil AP content in the 10–20 cm layer at 145–155 m (a.s.l.) of SB decreased by 90.90% in 2018. The total potassium (TK) content of AFD increased from 2009 to 2015 but decreased in 2018. However, the soil TK content in 2018 was significantly higher than that in 2009. In AFD, soil pH increased from 2009 to 2015 and reached a maximum value (8.21). In 2018, the pH value of some soil samples showed a downward trend but was always significantly higher than during 2009. Moreover, according to the redundancy analysis, the elevation, soil layer, and flood cycle could explain 56.64% of the changes in soil properties. Our study demonstrates that dam operation was an indirect driver for the changes in the temporal dynamics of soil nutrients, providing a reference for the management of the TGD.

Does the Geographic Difference of Soil Properties Matter for Setting Up the Soil Screening Levels in Large Countries Like China?

China issued the unified national soil screening levels (NSSLs) in 2018 to assist the regulation of contaminated sites, but the applicability of NSSLs was not thoroughly evaluated. Datasets from the National Qinghai-Tibet Plateau Scientific Data Center indicated great variability of soil organic matter (0.8-173 g/kg), soil water content (0.05-0.6), soil porosity (0.4-0.6), and soil bulk density (1.11-1.59 kg/m3). We analyzed the effects of soil properties on the derivation of SSLs by using Monte Carlo simulations. The soil factors mainly affected the inhalation exposure pathway of volatile organic compounds (VOCs). They had an effect of more than two orders of magnitude on SSLs for most selected VOCs, particularly with the parameters 0.35 > Henry's law constant > 0.1 and carbon-water distribution coefficient of >100. We compared NSSLs with the recommended SSLs assuming fifth percentile by using Monte Carlo simulations. In general, NSSLs were not sufficient to identify contaminated sites that require additional investigation in the south, central, and northwest regions but were too conservative in screening sites out that required no further action in the east and northeast regions. Our framework and findings may contribute to more scientific and effective soil quality management in other large countries.

The interpolation methods and neural network to estimate the spatial variability of soil organic matter affected by land use type

The use of geostatistical methods and artificial neural network (ANN), which can predict the spatial variability of SOM is of outmost significance. The present research investigated the effects of different land use types at different altitudes on the spatial variability of SOM using the above-mentioned methods. A total of 249 combined samples from the depth of 0–15 cm on the basis of land use type and topography were collected from different parts of the research area (9545 km2). Using cross validation, the methods were compared and the best fitted one was selected on the basis of mean error (ME) and root mean square error (RMSE). The best-fitted semivariogram models for SOM (R2= 0.894) and SOC (R2= 0.761) were spherical. The cross-validation method indicated ANN as the most accurate method for the prediction of SOM and SOC with the ME and RMSE of 0.37 and 0.36 for SOM, and 0.37 and 0.35 for SOC, respectively.

Soil organic matter and geochemical characteristics shape microbial community composition and structure across different land uses in an Australian wet tropical catchment

AbstractSoil characteristics vary across land uses, and soil microbial communities are likely to respond to such variations. However, such responses are not well understood. In this study, we examined how specific changes in soil organic matter (SOM) and geochemical characteristics influenced the bacterial and fungal community composition and structure across land uses. Soil samples were collected from grazing, sugarcane, forest and banana land uses in the Johnstone River catchment (wet tropics), northeastern Australia. High‐throughput sequencing of 16S rRNA gene and ITS amplicons were employed to characterise soil bacterial and fungal communities, respectively. Bacterial and fungal α‐diversity did not differ across land uses, whereas strong shifts in their community composition and structure were evident. Patterns in microbial community composition were strongly (p < 0.01) associated with changes in soil total organic carbon and nitrogen (TOC and TN), TOC:TN ratio, hot‐water extractable organic carbon (HWEOC) and soil geochemical characteristics (e.g., pH, Co, Mn, K, Mg and P contents). Distinct bacterial community composition was observed in forest soils, while fungal communities were similar in banana and forest soils compared with sugarcane and grazing. Overall, our results showed that SOM characteristics were the major driver on structuring soil microbial communities across land uses, while soil bacterial communities were more sensitive to variations in SOM and geochemical characteristics compared with fungi. Results from this study have implications for developing more targeted management practices across different land uses to improve soil health and ecosystem function in a wet tropical area.

Spatial variability of soil organic matter and total nitrogen and the influencing factors in Huzhu County of Qinghai Province, China

ABSTRACT The soil organic matter (SOM) and soil total nitrogen (STN) is a significant concern in Qinghai–Tibet Plateau, China. This study analysed the spatial distribution of SOM and STN and determined their influencing factors to support the conservation of cultivated soil and development of sustainable agricultural strategies in the Plateau. In total, 120 soil samples were collected from the 0–20-cm soil layer in Huzhu County, Qinghai Province. Traditional statistical and geostatistical methods were used to analyse the spatial distribution of SOM and STN; a geographical detector (GeoDetector)was used to explore the factors influencing the spatial variation. The SOM and STN concentrations were 6.92–44.57 and 0.52–2.54 g/kg, respectively. The Cokriging interpolation map showed a similar spatial distribution pattern for SOM and STN concentrations, which decreased from the northeast to southwest directions in the study area. GeoDetector results revealed that the three primary factors influencing the spatial variability of SOM and STN were soil type, annual accumulated precipitation and elevation, with their explanatory power ranging between 38.4% and 59.5%. Two-factor interactions enhanced the explanatory power of the spatial variability of SOM and STN. The research results provide a reference for conservation tillage and precision agriculture.

Long-term bare fallow soil reveals the temperature sensitivity of priming effect of the relatively stabilized soil organic matter

Background and aimsPriming effect (PE) plays an important role in modifying the decomposition of soil organic matter (SOM), but large uncertainties remain in the temperature effect on PE mainly due to the variation in SOM stability.MethodsWe tested the temperature effect on PE of the relatively stabilized SOM by incubating soils collected from a bare fallow (representing the relatively stabilized SOM) and its adjacent old field (containing both stabilized SOM and labile SOM) at 10 and 20 °C for 815 days. We used a natural 13C abundance tracer method for measuring the PE.ResultsPositive PE was observed in all treatment combinations when maize leaf litter was added. The temperature sensitivity of PE in the bare fallow soil and the old field soil was quite different: increasing temperature significantly enhanced the magnitude of PE in the bare fallow soil, but had no effect on it in the old field soil. The increase of microbial biomass C by litter addition was higher in the bare fallow soil than in the old field soil. Furthermore, for litter-treated soil, temperature increase significantly increased net N mineralization rate throughout the incubation in the bare fallow soil, but had minor effect on it in the old field soil at the end of incubation.ConclusionsOverall, this study demonstrates that PE of the relatively stabilized SOM is sensitive to temperature, which may be mainly driven by greater microbial growth and demand for N.

Hairy vetch role to mitigate crop yield gap in different yield environments at field level

The spatial variability of soil organic matter, plant water availability, and soil nitrogen (N) availability are drivers of crop response to mineral N fertilizer. The complex interaction of these factors is responsible for within-field corn and wheat yield variability. The hairy vetch (HV) cover crop is an economic, environmentally friendly, and efficient N source capable of conciliating crop yield and soil health. Nevertheless, the HV effects to mitigate yield gap of management zone (MZ) have not yet been documented. This study evaluated the effects of HV and mineral N fertilization, in single or combined input, on alleviating crop yield gap. The study was carried out in two croplands southern Brazil. The experimental design was a complete randomized block in a split plot having MZ (high, medium, and low yield) and N fertilizer rates. Wheat and corn N uptake and grain yield had a quadratic adjustment with N fertilizer input, but there was a significant MZ effect, where low yield zone (LYZ) was less responsive. Consequently, mineral N fertilization as a single input to mitigate the yield gap in this MZ was not efficient. On the other hand, HV increased corn N uptake and grain yield mainly in LYZ compared to MYZ and HYZ. HV fully mitigated the yield gap between MYZ and HYZ. The combined use of HV and mineral N fertilization rate adjusted according to N legume credit and MZ was an efficient strategy to boost yield, favoring soil health and environmental protection.

An inverse dielectric mixing model at 50 MHz that considers soil organic carbon

Abstract. The prevalent soil moisture probe algorithms are based on a polynomial function that does not account for the variability in soil organic matter. Users are expected to choose a model before application: either a model for mineral soil or a model for organic soil. Both approaches inevitably suffer from limitations with respect to estimating the volumetric soil water content in soils with a wide range of organic matter content. In this study, we propose a new algorithm based on the idea that the amount of soil organic matter (SOM) is related to major uncertainties in the in situ soil moisture data obtained using soil probe instruments. To test this theory, we derived a multiphase inversion algorithm from a physically based dielectric mixing model capable of using the SOM amount, performed a selection process from the multiphase model outcomes, and tested whether this new approach improves the accuracy of soil moisture (SM) data probes. The validation of the proposed new soil probe algorithm was performed using both gravimetric and dielectric data from the Soil Moisture Active Passive Validation Experiment in 2012 (SMAPVEX12). The new algorithm is more accurate than the previous soil-probe algorithm, resulting in a slightly improved correlation (0.824 to 0.848), 12 % lower root mean square error (RMSE; 0.0824 to 0.0727 cm3 cm−3), and 95 % less bias (−0.0042 to 0.0001 cm3 cm−3). These results suggest that applying the new dielectric mixing model together with global SOM estimates will result in more reliable soil moisture reference data for weather and climate models and satellite validation.

Variation of soil organic matter depends on light-fraction organic matter under long-term monocropping of different crops

Cultivating crops influences soil organic matter (SOM), but the effect of different crops remains unclear, particularly under long-term monocropping. The objective of this study was to identify how different crops influence the content and chemical structures of SOM under long-term monocropping. Here, soils were sampled (0–20 cm) under 27-year soybean and maize monocropping and separated into different physical fractions. The content and chemical structures of SOM in all fractions were determined. SOM contents were higher under soybean than maize in bulk soil and macroaggregates and their light-fractions instead of microaggregates and silt and clay. The difference in SOM chemical structure was observed in aggregates and density fractions rather than bulk soils and supported by the result of principal component analysis. The proportion of O-alkyl C in macro- and microaggregates and all free light fractions and that of aromatic C in mineral-associated fractions were higher, while that of carbonyl C was lower under maize than soybean. These results demonstrated that different crops monocropping influences the content and chemical structures of SOM, and the variations were mainly in the light-fraction SOM and highlight a higher sensitivity of physical fractions than bulk soil to different crops.

Multi-Proxy Study of a Holocene Soil Profile from Romania and Its Relevance for Speleothem Based Paleoenvironmental Reconstructions

In this study, we describe a sedimentary deposit situated above Ascunsă Cave (SW Romania) that should be in depositional connection with coeval stalagmites from the cave. We excavated a 2.5 m deep soil profile and took contiguous bulk samples every 5 cm. Soil samples were analyzed for clay mineralogy, grain size, chemical composition, magnetic susceptibility, and stable carbon isotopes. Radiocarbon dating revealed that the soil is of Holocene age, and presents a depositional hiatus between 5.4 and 2.3 thousand years before the present. Due to the open system behavior of soils and mobility of organic matter, a few hundred years of uncertainty should be considered for the duration of this hiatus. The fine fraction is dominated by silt, while the clay mineralogical association is made of illite, chlorite, kaolinite, vermiculite, and illite–vermiculite and illite–chlorite mixed layered minerals. The sediment source of this soil is represented by a nearby mélange complex, as well as by an underlying terra rossa-type soil. As this latter type of soil is widespread in our study area, we performed luminescence dating on two samples from a representative location close to our site and it appears that this type of Mediterranean soil was formed during the Last Interglacial period. δ13C variability in soil organic matter and a stalagmite from the cave are comparing well, and could help future studies identify modifications in isotopic fractionation processes within the cave.

Incorporation of high accuracy surface modeling into machine learning to improve soil organic matter mapping

Digital soil mapping approaches related to soil organic matter (SOM) are crucial to quantify the process of the carbon cycle in terrestrial ecosystems and thus, can better manage soil fertility. Recently, many studies have compared machine learning (ML) models with traditional statistical models in digital soil mapping. However, few studies focused on the application of hybrid models that combine ML with statistical models to map SOM content, especially in loess areas, which have a complicated geomorphologic landscape. In this study, the trend prediction used two ML models, i.e., gradient boosting modeling and random forest (RF), and a traditional stepwise multiple linear regression plus interpolated residuals generated from two classic geostatistical models, i.e., ordinary kriging and inverse distance weighting, and a high accuracy surface modeling (HASM) were implemented to map SOM content in the Dongzhi Loess Tableland area of China. A total of 145 topsoil samples and heterogeneous environmental variables were collected to develop the hybrid models. Results showed that 18 variables related to soil properties, climate variables, terrain attributes, vegetation indices, and location attributes played an important role in SOM mapping. The models that incorporate ML algorithms and interpolated residuals to predict SOM variation were found to have a better ability to handle complex environment relationships. The HASM model outperformed traditional geostatistical models in interpolating the residuals. In contrast, RF combined with HASM residuals (RF_HASM) gave the best performance, with the lowest mean absolute error (1.69 g/kg), root mean square error (2.30 g/kg), and the highest coefficient of determination (0.57) and concordance correlation coefficient (0.69) values. Moreover, the spatial distribution pattern obtained with RF_HASM yielded a spatial distribution of SOM that better fit the actual distribution pattern of the study area. In conclusion, these results suggest that RF_HASM is particularly capable of improving the mapping accuracy of SOM content at the regional scale.

Bivariate empirical mode decomposition of the spatial variation in the soil organic matter content: A case study from NW China

Soil organic matter (SOM) significantly affects soil quality, food security, and the global carbon cycle. Variations in the SOM content are controlled by many environmental factors and differ depending on the scale and region. The Mountain–Oasis–Desert System (MODS) in arid areas is limited by climate conditions and water and soil resources. Therefore, the effects of environmental factors on the SOM content may differ. This study aimed to characterize spatial patterns of the SOM content in a MODS in northwestern China and reveal the SOM variations at different scales and regions. First, we employed the random forest model to predict the SOM content and the uncertainty at the nodes of a 90 m grid. We then extracted scale- and region-dependent variations in the SOM content using bivariate empirical mode decomposition (BEMD). Data analysis, geostatistics, and a two-dimensional fast Fourier transform were applied to analyze the SOM pattern and its correlations with environmental covariates at different scales. The results suggest that the variations in the SOM content were mainly concentrated at the large scale. The correlations between environmental factors and the SOM were scale- and region-specific, indicating complex interrelationships among topography, land use, salinization, soil erosion, and fertilization. At the intermediate-large spatial scale, topography characteristics and surface features were the major factors controlling the SOM content, whereas the climate had a larger effect on SOM than other factors at the large scale. Based on our results, BEMD has great potential to reveal SOM variations in the scale–region domain. BEMD can be used in environmental modeling in the future to gain insights into the variations in soil properties depending on environmental factors and anthropogenic activities.