Distribution Of Soil Organic Matter Research Articles

Unveiling soil microbial community dynamics in desertification: A case study from the tianshan mountains, xinjiang

Microorganisms constitute the primary ecological group in desertified soil, driving soil biogeochemical cycles. Understanding their structure and diversity is crucial for the ecological restoration of desertification areas. This study focuses on the typical desert slopes in the eastern section of the northern foothills of the Tianshan Mountain in Xinjiang, China. It investigates changes in the main prokaryotic microbial community structure, including bacteria and archaea, and their relationship with environmental factors. Results show generally low soil water content, increasing with depth, and a weak alkaline nature with no significant vertical variations. While microbial communities do not distinctly respond to soil water content, there are noticeable variations in microbial diversity with clear stratification, negatively correlated with total organic carbon. Molecular ecological network analyses of five bare soil profiles in Xinjiang’s Hami Tianshan Desert reveal positive interactions among soil microorganisms in vertical layers. Surprisingly, the topsoil, despite having complex networks, shows low diversity and weak interconnectivity. Intriguingly, subsurface soils exhibit distinct molecular ecological networks similar to those found in ecological transition zones (These zones are characterized by rapid environmental shifts from one type to another), further highlighting significant ecological disparities between surface and subsurface soils. By analyzing the structure of soil microbial communities and their relationship with environmental factors on typical desert slopes in the Tianshan Mountains of Xinjiang, significant microbial ecological differences are found between surface and subsurface soils. Notably, in subsurface soils, microbial networks similar to those in ecological transition zones are discovered. These areas have complex microbial community structures and diverse ecological functions, providing new insights into the mechanisms of ecological restoration in desertified soils. Consequently, this study suggests that the vertical distribution patterns of microbial communities in bare desert soil areas differ from those in vegetated areas such as forests and grasslands. The interactions among species, the unique distribution of soil organic matter, and various physical factors are likely pivotal in the distribution patterns of microbial communities in bare desert soils. This research provides foundational theories and empirical support for the ecological restoration of bare desert soils.

Analyzing Spatial Distribution and Influencing Factors of Soil Organic Matter in Cultivated Land of Northeast China: Implications for Black Soil Protection

Soil organic matter (SOM) in cultivated land is vital for land quality and food security. This study examines SOM distribution and influencing factors in northeastern China, providing insights for sustainable agriculture. Utilizing 10 m resolution SOM data, the analysis covers regions including the Greater and Lesser Khingan Mountains, Liaohe Plain, Sanjiang Plain, Songnen Plain, the northwest semi-arid region, and the low hilly areas of Paektu Mountain. The Geodetector method is employed to assess various influencing factors. The key findings are as follows: (1) The average SOM content in Northeast China (37.70 g/kg) surpasses the national average, is highest in the Greater and Lesser Khingan Mountains (49.32 g/kg), and lowest in the northwest semi-arid region (26.15 g/kg). (2) SOM content is maximized in regions with high altitudes, steep slopes, low temperatures, and moderate precipitation. (3) The annual average temperature is the primary factor influencing SOM distribution, with a combination of temperature and administrative divisions providing better explanatory power. (4) SOM trends vary across protected areas, with slope being critical in semi-humid plains, elevation in arid regions, and no dominant factors identified in the Sanjiang Plain. These findings underscore the need for tailored black soil protection policies to effectively leverage local resources and preserve ecosystem integrity.

In situ visualization on the mobility of mineral associated organic matter in a microfluidics-based heterogeneous soil aggregate structure

The mobility of different types of soil organic matter (SOM), including dissolved organic matter (DOM) and mineral associated organic matter (MAOM), along the soil profile in response to infiltration flow can significantly affect the carbon pools in subsoil. In addition, the soil heterogeneity on the pore-scale, comprising pore spaces within the intra-aggregate matrix and preferential pathways within inter-aggregate zones, can also affect the mobility of SOM. This is especially the case for clay-sized MAOM. However, the quantitative understanding of the clay-sized MAOM mobility in presence of pore-scale heterogeneity remains elusive. This study designed a novel microfluidic device to track the pore-scale distribution and mobility of fluorescence labeled clay-sized MAOM in heterogeneous soil pore structures. Results showed that the MAOM mobility was significantly lower in the intra-aggregate zones compared with in the inter-aggregate zones. Only 4.8 % to 47.9 % of MAOM in the intra-aggregate region was mobilized at high leaching flow rates of 81 to 163 mm/d. Moreover, colloid mobilization induced by shearing rather than solute desorption followed by solute dilution is expected to be the major factor facilitating the mobilization of clay-sized MAOM. This study demonstrated a new experimental protocol allowing the in-situ visualization and quantification of OMs mobility in presence of pore-scale heterogeneity. Moreover, our findings also highlighted the potential importance of heterogeneous mobility of clay-sized MAOMs that facilitated by the colloid transport on the SOM turnover and distribution.

Rapid monitoring of the spatial distribution of soil organic matter using unmanned aerial vehicle imaging spectroscopy

ABSTRACT An existing technique for quickly obtaining the spatial distribution data of soil organic matter (SOM) involves combining an unmanned aerial vehicle (UAV) platform and imaging spectroscopy technology; this technique is suitable for precision agricultural management and real-time monitoring. In this study, the average spectral data were first extracted via circular regions of interest (ROIs) of different radii and modelled using the measured SOM to determine the optimal extraction range for the spectral data. Partial least-squares regression (PLSR) and support vector machine (SVM) models were constructed based on the raw spectra, standard normal transform spectra, multiple scattering-corrected spectra, and first-order differential spectra. The difference index, ratio index, and normalized index were calculated and analysed for their correlation with SOM content, and the spectral indices were screened to establish the SOM prediction model. Finally, the accuracy of the model was evaluated, and we selected the optimal model to map the spatial distribution of SOM. The results of our study show that 1) the radius of the ROI significantly affects the model’s accuracy; 2) the accuracy of the nonlinear SVM model is much higher than that of the linear PLSR model; 3) the prediction model based on spectral indices is better than full-band model. This study can serve as a model for the application of UAV imaging spectroscopy technology to field-scale SOM estimations and rapid monitoring, as well as provide technical support for precision agriculture and climate change studies.

Optimization Study of Soil Organic Matter Mapping Model in Complex Terrain Areas: A Case Study of Mingguang City, China

Traditional soil organic matter mapping is mostly polygonal drawing, which is even more difficult to accurately depict in complex terrain areas. The spatial distribution of soil organic matter is closely related to agricultural production, natural resources, environmental governance, and socio-economic development. Efficiently, economically, and accurately obtaining information on changes in soil organic matter in areas with diverse topography is an urgent problem to be solved. Mingguang City has a high research value because of its unique topography and natural landscape. To solve the problem of soil organic matter mapping in this area, this study will construct an excellent organic matter prediction model. Using 173 soil survey samples (123 for training and 50 for testing), the optimal feature variable subsets selected from 31 environmental variables through Pearson correlation, stepwise regression-variance inflation factor, and recursive feature elimination models based on different algorithms were employed. Each selected feature subset was then used to construct organic matter prediction models using multiple advanced machine learning algorithms. By comparing accuracy validation and model performance, the organic matter prediction model suitable for Mingguang City (RFE-RF_SVM) was obtained, that is, the prediction model of organic matter based on support vector machines with the feature variables screened by the feature recursive elimination algorithm of random forest with RMSE = 3.504, VSI = 0.036, and R-squared = 0.730. Furthermore, the analysis focused on assessing the significance of the predictive factors. The mapping results of this study show that the soil organic matter content in the central and northwestern parts of the study area is low, and the reasons for this situation are different. The central part is mainly caused by the change of land use and topography, while the northwestern part is caused by the loose soil structure caused by the parent material. The government can take targeted measures to improve the soil in the areas with poor organic matter.

Assessing Soil Prediction Distributions for Forest Management Using Digital Soil Mapping

Texture, soil organic matter (SOM), and soil depth (SoD) are crucial properties in forest management because they can supply spatial information on forest site productivity and guide fertilizer applications. However, soil properties possess an inherent uncertainty that must be mapped to enhance decision making in management applications. Most digital soil mapping predictions primarily concentrate on the mean of the distribution, often neglecting the estimation of local uncertainty in soil properties. Additionally, there is a noticeable scarcity of practical soil examples to demonstrate the prediction uncertainty for the benefit of forest managers. In this study, following a digital soil mapping (DSM) approach, a Quantile Regression Forest (QRF) model was developed to generate high-resolution maps and their uncertainty regarding the texture, SoD, and SOM, which were expressed as standard deviation (Sd) values. The results showed that the SOM (R2 = 0.61, RMSE = 2.03% and with an average Sd = 50%), SoD (R2 = 0.74 and RMSE = 19.4 cm), clay (R2 = 0.63, RMSE = 10.5% and average Sd = 29%), silt (R2 = 0.59, RMSE = 6.26% and average Sd = 33%), and sand content (R2 = 0.55, RMSE = 9.49% and average Sd = 35%) were accurately estimated for forest plantations in central south Chile. A practical demonstration of precision fertilizer application, utilizing the predictive distribution of SOM, effectively showcased how uncertainty in soil attributes can be leveraged to benefit forest managers. This approach holds potential for optimizing resource allocation and maximizing economic benefits.

Predicting wetland soil properties using machine learning, geophysics, and soil measurement data

PurposeMachine learning models can improve the prediction of spatial variation of wetland soil properties, such as soil moisture content (SMC) and soil organic matter (SOM). Their performance, however, relies on the quantity of data used to train the model, limiting their use with insufficient data. In this study, we assessed the use of synthetic data constrained by limited field data for training an eXtreme Gradient Boosting (XGBoost) algorithm used to predict the distribution of soil properties based on geophysical measurements constrained by soil samples.Materials and methodsA spatial distribution of soil apparent electrical conductivity (ECa) and laboratory measurements of SOM and SMC from twenty-two core samples were acquired at the St. Michael restored wetland near Defiance, Ohio. The correlations between ECa, SOM, and SMC were explored for predicting the spatial distribution of SOM and SMC. We used a Beta Variational AutoEncoder (β-VAE) approach to synthetically generate over 70,000 training data from the original twenty-two data from soil cores. The training data samples were taken from the latent space. The XGBoost algorithm was then trained on the β-VAE generated data and used to predict the spatial distribution of SOM and SMC at the site. We also validated the accuracy of the XGBoost predictions using an original holdout model validation technique.Results and discussionsThe generated synthetic data using the β-VAE include both soil attributes and ECa, which are larger and more diverse than the original training set with an absolute mean reconstructed error for SMC and SOM ranging from 0.018 to 0.022 and 0.026 to 0.041, respectively. This indicates that the β-VAE successfully generated a realistic synthetic dataset and overcame the technical barrier of using limited datasets. In addition, using generated data to expand the original training data helps the XGBoost model make more accurate predictions compared to training on the original data. The XGBoost prediction performance yielded average Lin’s concordance correlation coefficient (LCCC) values of 0.82 and 0.85 for SOM and SMC and a ratio of performance to deviation (RPD) values of 1.92 and 2.22 respectively, indicating a good performance.ConclusionsThis study validated the use of β-VAE to successfully generate synthetic wetland soil datasets with attributes of the original field data that can be effectively used to train the machine learning XGBoost model. The proposed framework offers an efficient solution for mapping the spatial variability of soil property in data-scarce wetland soil environments.

Пространственное моделирование органического вещества почвы опытных делянок на основе данных с БПЛА

The article presents the results of spatial geostatistical and regression modeling of soil organic matter in a section of an experimental field on the territory of the Educational, Scientific and Experimental Field of the Perm State AgroTechnical University. The purpose of the research is to model the spatial distribution of soil organic matter in the experimental plot using UAV survey data. The research area covers an area consisting of 20 plots of one of the experimental fields with an area of 0.34 hectares, on which a UAV survey was carried out on September 12, 2023 and the content of organic matter was analyzed. Spatial modeling was carried out using geostatistical and regression methods based on 20 points representing the centroids of experimental plots. As a result of the research, an average relationship between the content of organic matter and elevation and distance to watercourses was established. With regression modeling, the range of values of the distribution of organic matter is narrowed by approximately 0.5% from the minimum and maximum compared to the geostatistical model. Keywords: UAV, GEOINFORMATIONAL MAPPING, SOIL ORGANIC MATTER

Soil Organic Matter and Soil Organic Carbon in Barito Delta

Soil organic matter and soil organic carbon is essential indication of soil carbon stock in terrestrial ecosystem particularly in peatland. Spatial distribution of soil organic carbon and soil organic matter is primary assessment to predict future soil condition under climate change traits. Barito Delta is one of peatland areas in Kalimantan Island that experiences urban expansion. It is necessary to assess distribution of soil organic carbon and soil organic matter to define the potential traits of urbanization and climate change to carbon stock condition in this delta. Therefore, the aim of this research is to analyze the distribution of soil organic matter and soil organic carbon in Barito Delta. Fieldwork measurement is performed to take sample of soil organic matter for each layer within 5 m soil depth. Inverse distance weighting interpolation is used to determine the spatial distribution of soil organic matter (SOM) and soil organic carbon (SOC) applying in R software.
 The value of SOM and SOC decrease along the increase of soil depth so that the largest percentage of SOM and SOC is in the first layer. Large value of SOM and SOC are mostly in the southern part of Barito Delta since it is bordered with sea water and downstream of rivers. The maximum value of SOM is ~12 % in the first layer, while 8.3 % of SOC in the first layer. Nevertheless, the value of SOC decrease in the near future, it is as indication Barito Delta contribute to carbon stock loss realizing to atmosphere that can rise greenhouse gasses and climate change.

Mapping Soil Organic Matter Using Different Modeling Techniques in the Dryland Agroecosystem of Huang-Huai-Hai Plain, Eastern China

Accurately mapping the spatial distribution and variation of soil organic matter (SOM) is of great significance for guiding regional soil management. However, the applicability and prediction performance of machine learning techniques in dryland agroecosystems still needs to be further studied. In this study, we collected a total of 733 topsoil samples from the farmland in Xiao County, Anhui Province, which is a typical dryland agroecosystem in the Huang-Huai-Hai Plain. Then, the environmental covariates were selected, and the ordinary kriging (OK), multiple linear stepwise regression (MLR), regression kriging (RK), radial basis function neural network (RBFNN), and random forest (RF) models were conducted to map the SOM content, and the optimal model was ascertained. The results demonstrated that the alkali-hydrolyzable nitrogen (26.11%), available potassium (17.73%), mean annual precipitation (13.26%), and pH (11.80%) were the main controlling factors affecting the spatial distribution of SOM in the study area. Meanwhile, the introduction of environmental covariates can effectively improve the SOM prediction accuracy, and the RF model (R2 = 0.48, MAE = 2.38 g kg−1, MRE = 12.99%, RMSE = 3.14 g kg−1) has a better performance than the RFBNN, MLR, RK, and OK methods. Although there are local differences in the spatial distribution of SOM predicted by the five methods, the overall spatial distribution of SOM was characterized by the low concentration area (13.44–20.00 g kg−1) distributed in the central and northwest of study area, and the high concentration area (24.00–28.95 g kg−1) distributed in the southeast. Overall, our study demonstrated that machine learning-based models could accurately predict the SOM content in dryland agroecosystem, and the produced maps function as baseline maps for sustainable agricultural management.

County-Scale Spatial Distribution of Soil Nutrients and Driving Factors in Semiarid Loess Plateau Farmland, China

Characterized by a topography of thousands of ravines, the Loess Plateau has highly complex spatial variability in terms of soil nutrients. Therefore, it is of considerable importance to study the soil nutrient spatial distribution, driving factors of precise fertilizer management, and the strategic use of soil nutrient resources. In 2017, 242 soil samples were taken from the semiarid Anding district farming region in northern China. The spatial variability and factors influencing soil nutrients were studied using statistical and geostatistical methods. The results showed that the mean soil organic matter (SOM), total nitrogen (TN), available phosphorus (AP), available potassium (AK), and pH values were averaged at 12.64 g·kg−1, 0.84 g·kg−1, 23.20 mg·kg−1, 188.87 mg·kg−1, and 8.60, respectively. The nugget-to-sill ratios for the semi-variograms of SOM, TN, AP, and AK varied from 25.84 to 49.93%, while the coefficients of variation varied from 24.53 to 69.44%, revealing that all four indicators exhibited considerable variability, and that the samples’ geographical variability was produced by a combination of random and structural factors. Overall increasing trends were exhibited from the middle to the northeast and southwest in the distributions of SOM, TN, and AP. The spatial distribution of AK displayed the opposite trend, increasing from the southwest to north and southeast. The texture of the tillage layer was the main factor directly affecting SOM, and explained 8% of its variation. The distribution of TN was mainly influenced by the irrigation method and water source type. AP and AK contents differed significantly between the two parent materials, three textures, and three topography types at the level of p < 0.01. In conclusion, the regional soil fertility was poor, spatial heterogeneity was moderate, and influencing factors were complex, highlighting the need to adopt precise fertilization management and adopting land management measures according to the actual influencing factors of each nutrient, thereby contributing to the enhancement of regional fertility.

Analysis of soil organic matter influencing factors in the Huangshui River Basin by using the optimal parameter-based geographical detector model

To quantitatively analyze the spatial distribution of soil organic matter (SOM) content and its main influencing factors in the Huangshui River Basin, Qinghai Province, China, a total of 13 factors including topography, climate, soil properties, and vegetation cover were selected. The influence of these factors on the spatial distribution of SOM content was analyzed using the geographical detector model and the Pearson correlation coefficient method. The results showed that the average SOM content in the cultivated soil layer of the Huangshui River Basin was 28.38 g·kg−1. The results of factor detection and Pearson correlation analysis indicated that the three dominant factors causing differences in the spatial distribution of SOM content were mean annual temperature, altitude, and soil pH. Furthermore, SOM content was negatively correlated with temperature and positively correlated with elevation. When the factors interacted, they exhibited a type of bi-enhance or nonlinear-enhance. The interaction between two factors improved the explanatory power of SOM, indicating the complexity of factors causing spatial distribution differences in SOM. This study provides a favorable basis for precision fertilization, soil improvement, and of soil quality improvement in the Huangshui River Basin.

Spatial distribution of soil organic carbon content in the agricultural land uses: Case study at the territory of the Rahoveci municipality, Kosovo

Due to the soil formation factors and different geographic areas of Dukagjini Plain, particularly in Rahovec municipality, the variation of soil organic carbon is high. Soil organic carbon (SOC) has a crucial role in the determination of the physical, chemical and biological behaviour of the soil. The most common land use types of this area are vineyards, table grapes, horticulture such as peppers and cabbage, and arable lands such as maize, winter wheat, alfalfa, and meadows. Considering the lack of soil information data in Kosovo, it is necessary to have soil information about this territory. The main objectives of the present study are, therefore: i) to investigate and determine the concentration of the soil organic matter (SOM), SOC, nitrogen (N) and soil pH-H2O, using laboratory analysis, and ii) to show the spatial distribution of SOC, SOM, N and pH using the Kriging and inverse weighting interpolation methods. Spatial variability of soil chemical parameters such as SOM, SOC, N, and pH are important to be interpolated to view the changeable soil properties by kriging and inverse distance weighting method and to generate the continuous sample for site-specific management. Disturbed soil samples were collected from the top soil 0-30 cm and 30-60 cm depth, to determine selected soil chemical parameters, during June-July 2019. A large number of soil samples were collected, 2087 in the first horizon and 2065 in the second. The average of SOC of the first horizon was 0.91%, which variates from 0.07 to 4.06%, while in the second horizon was 0.0 to 2.84%, the average content of N in the first horizon was 0.09%, which variate from 0.01 to 0.60%, while in the second horizon was 0.0 to 0.39%, meanwhile, the average of soil pH-H2O in the first horizon was 7.67, which variates from 4.25 to 9.35, while in the second horizon was 7.79, which variate from 3.25 to 9.30.

Quantification of the effects of long-term straw return on soil organic matter spatiotemporal variation: a case study in a typical black soil region

Abstract. The straw return practice is essential to soil organic matter (SOM) accumulation in the black soil area with high carbon sequestration potential. However, due to lacking accurate spatial distribution of straw return, few studies have carried out rigorous research on the impact of long-term straw return on SOM spatiotemporal variation on a regional scale. This study was carried out across an approximately 3000 km2 area in Lishu County, northeastern China, a typical agricultural plain. Based on a total of 619 soil samples and 16 environmental covariates, the study mapped the spatial distributions of SOM in 2006 and 2018 by random forest (RF) and evaluated the effects of the interaction of soil properties, land use, and straw return on SOM spatial–temporal variation. The results show that in the context of long-term straw return, the mean SOM content increased from 18.93 to 20.84 g kg−1 during 2006–2018. And 74.49 % of the region had a significant increase (maximum of 24.41 g kg−1) of SOM. The severest SOM loss occurred in the northwest due to the light texture and the transition from paddy fields to dryland. Nevertheless, for areas from paddy fields to dryland, the SOM loss decreased with the increased cumulative crop residue coverage (CRC). The SOM even increased by 1.79 g kg−1 when the cumulative crop residue coverage reached 0.60–1.00. In addition, soil with higher initial SOM and sand content had a lower response to straw return. The study revealed that straw return is beneficial to carbon sink in farmland and is a better way to prevent a carbon source caused by the conservation of paddy field to dryland.

A Framework for Retrieving Soil Organic Matter by Coupling Multi-Temporal Remote Sensing Images and Variable Selection in the Sanjiang Plain, China

Soil organic matter (SOM) is an important soil property for agricultural production. Rising grain demand has increased the intensity of cultivated land development in the Sanjiang Plain of China, and there is a strong demand for SOM monitoring in this region. Therefore, Baoqing County of the Sanjiang Plain, an important grain production area, was considered the study area. In the study, we proposed a framework for high-accuracy SOM retrieval by coupling multi-temporal remote sensing (RS) images and variable selection algorithms. A total of 73 surface soil samples (0–20 cm) were collected in 2010, and Landsat 5 images acquired during the bare soil period (April, May, and June) were selected from 2008 to 2011. Three variable selection algorithms, namely, Genetic Algorithm, Random Frog and Competitive Adaptive Reweighted Sampling (CARS), were combined with Partial Least Squares Regression (PLSR) to build SOM retrieval models on the spectral bands and indices of the images. The results using a single-date image showed that the combination of variable selection algorithms and PLSR outperformed using PLSR alone, and CARS showed the best performance (R2 = 0.34, RMSE = 15.66 g/kg) among all the algorithms. Therefore, only CARS was applied to SOM retrieval in the different year interval groups. To investigate the effect of the image acquisition time, all images were divided into various year interval groups, and the resulting images were then stacked. The results using multi-temporal images showed that the SOM retrieval accuracy improved as the year interval lengthened. The optimal result (R2 = 0.59, RMSE = 11.81 g/kg) was obtained from the 2008–2011 group, wherein the difference indices derived from the images of 2009, 2010, and 2011 dominated the selected spectral variables. Moreover, the spatial prediction of SOM based on the optimal model was consistent with the distribution of SOM. Our study suggested that the proposed framework that couples stacked multi-temporal RS images with variable selection algorithms has potential for SOM retrieval.

Regional-Scale Topsoil Organic Matter Estimation Based on a Geographic Detector Model Using Landsat Data, Pingtan Island, Fujian, China

Understanding the spatial distribution of soil organic matter (SOM) is important for land use management, but conventional sampling methods require significant human and financial resources. How to map SOM and monitor its changes using a limited number of sample points combined with remote sensing techniques that provide long-time series data is crucial. This study aimed to generate a regional-scale near-surface SOM map using 70 soil samples and covariate environmental factors extracted mainly from Landsat 8 OLI. Firstly, the sensitivity of each environmental factor to SOM was tested using a geographic detector model (GDM). Secondly, the tested factors were selected for modeling and mapping by ordinary least squares (OLS) and geographically weighted regression kriging (GWRK). The performance of these two models was compared. Finally, the mapping results of the better model (GWRK) were compared and analyzed with the traditional interpolation results based solely on sampling points to verify the rationality of the proposed method. The results show that three environmental factors, ratio vegetation index (RVI), differential vegetation index (DVI), and terrain roughness (TR), have a strong influence on the spatial variability of SOM. Using these three factors in combination with the GWRK method, a more accurate and refined spatial distribution map of SOM can be obtained. Comparing the SOM maps of GWRK and the traditional interpolation method, the results show that the accuracy of GWRK (R2 = 0.405; mean absolute error = 0.637, and root mean square error = 0.813) is higher than that of traditional interpolation methods (R2 = 0.291, MAE = 0.609, and RMSE = 0.863). The spatial recognition rate (fineness) of SOM patches at all levels using the GWRK method increased by more than 73 times compared to the traditional kriging. We conclude that the combination of limited SOM samples, environmental variables, GDM, and GWRK is a pragmatic approach for estimating regional-scale SOM.

Optimal soil organic matter mapping using an ensemble model incorporating moderate resolution imaging spectroradiometer, portable X-ray fluorescence, and visible near-infrared data

Digital soil mapping of soil organic matter (SOM) is necessary because of its importance for carbon sequestration, soil health, and food security. However, large-scale digital soil mapping of SOM remains a challenge to improve accuracy and provide more local information. To address this knowledge gap, a strategy that incorporatesremoteandproximalsensingdataintoanensemblemodelforthe digital soil mapping is proposed. In this study, moderate resolution imaging spectroradiometer, portable X-ray fluorescence, and visible near-infrared spectroscopy data from 402 soil samples were used to map the SOM at a resolution of 90 m, model the SOM with random forest, cubist, and ensemble models, and evaluate its environmental factors in the northeastern and northern Chinese plains, a typical agricultural plain (approximately 66,000,000 ha). The digital maps of the SOM were derived along with their uncertainties. The results show that the visible near-infrared and portable X-ray fluorescence data play an important role in the SOM distribution. Inclusion of these data in the model, improved the R2p by 6.25–35.42 %, and reduced the root mean square error of the prediction by 0.30–1.54 g kg−1. The ensemble model, which included remote and proximal sensing variables, outperformed the results of previous studies with a root mean square error of 6.68 g kg−1 and provided more detailed information. Thus, this study confirmed the effectivenessoftheproposedstrategy. The SOM product obtained by this strategy was able to accurately control soil management, and the structural equation model showed that human activities exerted a direct influence on SOM comparable to the overall effect of natural factors on SOM. The contribution of straw mulching to human activities was great with path coefficient >0.50. This suggests that land management, especially straw mulching, should be improved in this area. In summary, this study proposed a novel strategy for accurately and efficiently obtaining SOM products for agricultural decision-making, terrestrial carbon cycling, and carbon stock estimation. Future advancements will focus on more accurate and detailed global SOM maps or carbon storage by integrating multiple models with remote sensing data and proximal sensing data from available soil spectral libraries.

The effects of biochar on soil organic matter pools are not influenced by climate change

The sustainability of Mediterranean croplands is threatened by climate warming and rainfall reduction. The use of biochar as an amendment represents a tool to store organic carbon (C) in soil. The vulnerability of soil organic C (SOC) to the joint effects of climate change and biochar application needs to be better understood by investigating its main pools. Here, we evaluated the effects of partial rain exclusion (∼30%) and temperature increase (∼2 °C), combined with biochar amendment, on the distribution of soil organic matter (SOM) into particulate organic matter (POM) and the mineral-associated organic matter (MAOM). A set of indices suggested an increase in thermal stability in response to biochar addition in both POM and MAOM fractions. The MAOM fraction, compared to the POM, was particularly enriched in labile substances. Data from micro-Raman spectroscopy suggested that the POM fraction contained biochar particles with a more ordered structure, whereas the structural order decreased in the MAOM fraction, especially after climate manipulation. Crystalline Fe oxides (hematite) and a mix of ferrihydrite and hematite were detected in the POM and in the MAOM fraction, respectively, of the unamended plots under climate manipulation, but not under ambient conditions. Conversely, in the amended soil, climate manipulation did not induce changes in Fe speciation. Our work underlines the importance of discretely taking into account responses of both MAOM and POM to better understand the mechanistic drivers of SOC storage and dynamics.

Determining soil health parameters controlling crop productivity in a Citrus Greening disease affected orange grove

Soil health is an important aspect for maintaining adequate crop production, but the specifics of what entails a healthy soil can vary from region to region and crop to crop. In highly managed agricultural systems, unhealthy soil can be masked by intensive management practices, yet there must be detrimental cutoff points in various characteristics, such as soil organic matter (SOM) concentrations, where even highly managed systems start to lose productivity. This negative impact was observed in a Florida citrus grove containing Valencia orange trees with observable differences in tree size yet were otherwise managed identically. A soil health index demonstrated that the areas with smaller trees had a significantly lower index score and those soils contained significantly less SOM (average SOM ​= ​0.57%) compared to areas with larger trees (average SOM ​= ​0.94%). The areas of lower crop productivity also had less enzymatic activity of common carbon-cycling enzymes and different microbial populations, which all together negatively affected soil health and corresponding plant productivity. This agricultural region is also known to have a Citrus Greening disease (HLB) infection rate of close to 100%, hence we developed a hypothesis that could explain how progression of this infection could be impacted by SOM concentrations and differences in microbial diversity. We posit that areas of this grove with healthier soil could have more resistance to the onset of fatal HLB symptoms. Consequently, soil organic matter distribution and concentration should be considered when establishing new groves in order to optimize soil and crop productivity.

The Quantified and Major Influencing Factors on Spatial Distribution of Soil Organic Matter in Provincial-Scale Farmland—A Case Study of Shandong Province in Eastern China

Soil organic matter (SOM) is an important component of soil and plays an important role in improving the soil’s physical and chemical properties. Ascertaining the spatial distribution of soil organic matter and its main controlling factors in the context of provincial scale farming is of important guiding significance for soil carbon sequestration, emission reduction and sustainable utilization. Using 257 soil profiles from the second soil survey in Shandong Province, GIS, we applied geostatistical methods to study the spatial distribution characteristics of SOM in topsoil. In addition, correlation and regression analyses were used to explore the main controlling factors over the spatial variation of SOM. The results showed that the mean amount of SOM in Shandong province ranged from 1.20–74.90 g·kg−1, with a coefficient of variation of 73.52%, which is a medium level of variation. The distribution of SOM in the study area was patchy, with higher levels of organic matter in the central, eastern, and northern parts, and lower levels of organic matter in the south-west. The comprehensive explanatory ability of all factors reached 52.30%. Soil type and parent material were the main controlling factors for the spatial variability of SOM in the Shandong Province, followed by soil texture and land use type, with topography and climatic factors having relatively little influence.