Variability Of Soil Organic Matter Research Articles

Spatial patterns of organic matter content in the surface soil of the salt marshes of the Venice Lagoon (Italy)

Abstract. Salt marshes are crucial eco-geomorphic features of tidal environments as they provide important ecological functions and deliver a wide range of ecosystem services. Being controlled by the interplay between hydrodynamics, geomorphology, and vegetation, the contribution of both organic matter (OM) and inorganic sediments drives salt marsh vertical accretion. This allows marshes to keep pace with relative sea level rise and likewise capture and store carbon, making them valuable allies in climate mitigation strategies. Thus, soil organic matter (SOM), i.e. the organic component of the soil, plays a key role within salt marsh environments, directly contributing to soil formation and supporting carbon storage. This study aims at inspecting spatial patterns of OM in surface salt marsh soils (top 20 cm), providing further insights into the physical and biological factors driving OM dynamics that affect salt marsh survival and carbon sink potential. Our results reveal two scales of variations in SOM content in marsh environments. At the marsh scale, OM variability is influenced by the interplay between surface elevation and changes in sediment supply linked with the distance from the marsh edge. At the system scale, OM content distribution is dominated by the gradient generated by marine and fluvial influence. The observed variations in SOM are explained by the combination of inorganic and organic input, preservation conditions, and sediment grain size. Our results highlight the importance of marshes as carbon sink environments, further emphasising that environmental conditions within a tidal system may generate strongly variable and site-specific carbon accumulation patterns, enhancing blue carbon assessment complexity.

AI can empower agriculture for global food security: challenges and prospects in developing nations.

Food and nutrition are a steadfast essential to all living organisms. With specific reference to humans, the sufficient and efficient supply of food is a challenge as the world population continues to grow. Artificial Intelligence (AI) could be identified as a plausible technology in this 5th industrial revolution in bringing us closer to achieving zero hunger by 2030-Goal 2 of the United Nations Sustainable Development Goals (UNSDG). This goal cannot be achieved unless the digital divide among developed and underdeveloped countries is addressed. Nevertheless, developing and underdeveloped regions fall behind in economic resources; however, they harbor untapped potential to effectively address the impending demands posed by the soaring world population. Therefore, this study explores the in-depth potential of AI in the agriculture sector for developing and under-developed countries. Similarly, it aims to emphasize the proven efficiency and spin-off applications of AI in the advancement of agriculture. Currently, AI is being utilized in various spheres of agriculture, including but not limited to crop surveillance, irrigation management, disease identification, fertilization practices, task automation, image manipulation, data processing, yield forecasting, supply chain optimization, implementation of decision support system (DSS), weed control, and the enhancement of resource utilization. Whereas AI supports food safety and security by ensuring higher crop yields that are acquired by harnessing the potential of multi-temporal remote sensing (RS) techniques to accurately discern diverse crop phenotypes, monitor land cover dynamics, assess variations in soil organic matter, predict soil moisture levels, conduct plant biomass modeling, and enable comprehensive crop monitoring. The present study identifies various challenges, including financial, infrastructure, experts, data availability, customization, regulatory framework, cultural norms and attitudes, access to market, and interdisciplinary collaboration, in the adoption of AI for developing nations with their subsequent remedies. The identification of challenges and opportunities in the implementation of AI could ignite further research and actions in these regions; thereby supporting sustainable development.

Three-dimensional space and time mapping reveals soil organic matter decreases across anthropogenic landscapes in the Netherlands

For restoring soil health and mitigating climate change, information of soil organic matter is needed across space, depth and time. Here we developed a statistical modelling platform in three-dimensional space and time as a new paradigm for soil organic matter monitoring. Based on 869 094 soil organic matter observations from 339,231 point locations and the novel use of environmental covariates variable in three-dimensional space and time, we predicted soil organic matter and its uncertainty annually at 25 m resolution between 0–2 m depth from 1953–2022 in the Netherlands. We predicted soil organic matter decreases of more than 25% in peatlands and 0.1–0.3% in cropland mineral soils, but increases between 10–25% on reclaimed land due to land subsidence. Our analysis quantifies the substantial variations of soil organic matter in space, depth, and time, highlighting the inadequacy of evaluating soil organic matter dynamics at point scale or static mapping at a single depth for policymaking.

Effective indicators and drivers of soil organic matter in intensive orchard production systems

Soil organic matter (SOM) is critical for the sustainable development of intense agroecosystems such as orchard production systems. Nevertheless, the status and drivers of SOM are indecipherable in different orchard systems due to the perennial anthropogenic influences and complex multicollinearity of soil properties. Thus, the present work aimed to address these issues using indicator transformation, data dimension reduction, and geostatistical method. We selected two orchard systems (open field and plastic shed) with similar mean SOM contents (approximately 15 g kg−1). Our results revealed that the average stratification ratio of SOM was significantly greater in open field orchards (2.20 ± 0.93) than in plastic shed orchards (1.75 ± 0.71) (p < 0.05). Likewise, the SOC/clay ratio in open field orchard soils (0.096 ± 0.051) was significantly higher than that in plastic shed orchards (0.088 ± 0.025) (p < 0.01). The variance analysis of these two indicators suggested a greater soil fertility in open field orchards than plastic shed. The main drivers of SOM in different orchard systems were different. In open field orchards, the terrain (q=0.36), soil ion processes (q=0.34), and soil types (q=0.31) were the dominant factors of affecting spatial variability of SOM, while the soil texture (q=0.44), soil types (q=0.32) and terrain (0.30) mainly explained the spatial heterogeneity of the SOM in plastic shed orchards. Outputs of this research provide a guidance for training the farmers to improve orchard SOM, as well as a reference for investigators to further explore the SOM influencing factors.

Variations of Soil Organic Matter and Nutrients in Diverse Hilly Soils of Bandarban, Bangladesh

Functioning and positive interactions in an ecosystem depend on physical and chemical features of soil, along with nutrient status. These soil nutrients are vital for the growth and development of plants, playing a crucial role in various essential functions. The current study investigates the dynamic changes in soil nutrient patterns across different land use scenarios and soil depths at Bandarban Sadar Upazila, Chittagong Hill Tracts, Bangladesh. The research focuses on how soil nutrient levels are distributed in two soil depths (0-15 cm and 15-30 cm) among three different land use practices, such as forest land, agricultural land and barren land. About 90 soil samples from the study area were collected using a stratified random sampling design. The analysis reveals significant variations in soil pH, organic matter (OM), total nitrogen (TN), phosphorus (P), potassium (K), and sulfur (S) levels among these land use types and soil depths. The findings revealed that agricultural land typically exhibited higher levels of soil S content and pH, in contrast to forested areas where OM, TN, P, and K levels were comparatively more abundant. Again, barren land showed a relatively higher pH and S content than agricultural land and forest land. Additionally, the topsoil (0-15 cm) generally showed higher nutrient content than the deeper soil layer (15-30 cm). The investigation uncovered significant positive correlations of OM with soil nutrients such as TN, P, K &amp; S, indicating mutual influences on their availability. Overall, the research outcomes emphasize the importance of sustainable land management practices to maintain soil fertility and optimize soil productivity in this ecologically significant region.

Spatial variation of soil organic matter and metal mobility in wetland soils: Implications for biogeochemical processes in lateritic landscape

The evolution of the lateritic landscape over time changes the path of pedogenesis and soil hydrology. The occurrence of upland wetlands in the savannah flat plateau transforms well-drained lateritic soils into hydric soils enriched in organic matter. We investigated the composition of soil organic matter (SOM) produced in a wetland and their link with the mineral constituents of soil and their contribution to the mobility of Si, Fe, and Al in the soil. Six tranches were opened in a soil catena located from the plateau toward the center of the wetland, representing different hydromorphic features. Soil morphology was described, and chemical, physical and mineralogical analyses were performed to evaluate soil properties. Chemical fractionation of Soil Organic Matter (SOM) was performed and total organic carbon (TOC), dissolved organic carbon (DOC), particulate organic carbon (POC), and water-extractable organic matter (WEOM) were determined in the soil horizons. Metal complexing properties of DOM were determined using an excitation-emission matrix of fluorescence. The occurrence of Fe eluviation in the superficial horizons, Fe2O3 enrichment in the mottled horizons, and ferricrete formation in the center of the wetland were observed. Geochemical transfer assessment and the variation of oxides within the horizons showed that Fe2O3 and SiO2 were more mobile than Al2O3, whose variability in the gley horizon reflected the mobility of Fe2O3 and SiO2. The stability constants of Al and Cu showed an increase with the depth for WEOM and SOM. The pattern of the organic carbon fraction varied according to the level of hydromorphy and soil depth. It was also observed an increase in the C/N ratio with depth, possibly related to the reduced bioavailability of DOC due to its adsorption with soil solids.

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.

Improving digital mapping of soil organic matter in cropland by incorporating crop rotation

Despite human activities are key influencing factors for cropland soil organic matter (SOM), detailed characterization of human activities has always been limited in the digital mapping of SOM due to the lack of proper representations of human’s cropland use activities. Crop rotation is an essential human agricultural practice significantly affecting the spatial–temporal variations of SOM due to the periodically dynamic changes of crops. Thus, incorporating crop rotation in the digital soil mapping holds high potential for improving SOM prediction. Here, we applied time-series radar Sentinel-1 and optical Sentinel-2 to map crop rotation systems by a hierarchical rule-based method. Then we explored the effectiveness of incorporating such information in predicting SOM by implementing various combinations of predictive variables. We chose a typical multiple cropping region with various crop rotations in southern China. The model performance was evaluated by 10-fold cross-validation. Results showed significant differences in SOM among the crop rotation systems, and the single rice rotated with vegetables has the highest SOM followed by the high-diversity vegetables and long-term orchard systems. Adding crop rotation enhanced the predictability of SOM with a decrease in RMSE by 7% and an increase in R2 by 24%. Furthermore, the crop rotation systems appeared more important in the predictive models than the soil, topographic, and climatic variables. Our results demonstrated the effectiveness of including crop rotation in predicting SOM over complex agricultural landscapes. Our study indicated that human activities should be characterized more detailedly in cropland soil mapping, and that crop rotation containing information on the seasonal dynamics of cropland may be an option for such characterization.

Contributions of climate and soil properties to geographic variations of soil organic matter across the East Asian monsoon region

Soil organic matter (SOM) plays a crucial role in soil functions and the global carbon cycle. Considering the disconnection of research emphasis between soil and ecology, little is known about the large-scale edaphic controls and their interaction with climate in SOM dynamics. This study aimed to explore the geographic evolution mechanisms of SOM from the perspective of soil intrinsic properties. A wide range of soil types from quaternary sediments were sampled from three ecosystems (forest, grass, and arable lands) and at different pedogenic horizons along the mid-temperate to tropical climate gradient in the East Asian monsoon region. SOM was measured in terms of organic carbon (SOC, 0.06–4.81 %), total nitrogen (TN, 0.04–0.41 %), and their ratio (C/N ratio, 1.17–12.26), as well as a series of soil properties: porosity, soil texture, clay mineralogy, exchangeable cations and soil aggregation. The indicators of SOM were most influenced by soil horizon (F=233.2–2375.2), followed by soil type (F=84.0–924.1, p < 0.001) and land use (F=4.7–92.2, p < 0.05); SOC, TN and C/N ratio overall decreased with increasing soil depth, and exhibited decrease first and then increase from north to south. Traditional predictors of soil texture and climate variables independently contributed little to SOM (p > 0.05). In contrast, bulk density had the greatest explanatory power (R2 = 0.379, p < 0.01), followed by soil aggregation (p < 0.05), while phyllosilicate minerals and iron oxides explained significant but small variances, all of which were climate-dependent. SOM could be jointly explained by soil properties (0.665) and climate (0.220) with an interaction effect of 0.152, among which bulk density, percentage of aggregate disruption, and mean annual temperature (MAT) were the most powerful predictors with negative effects. Structural equation modeling results indicated that MAT could influence SOM through soil aggregation and exchangeable cations. This study unravels the direct and indirect effects of climate on SOM and highlights the predominant roles of soil structural properties rather than abiotic primary agents at the regional scale, which merits consideration in improving SOM modeling and land management.

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.

Utilization of Livestock Waste as Biochar and Poschar to Increase Soil Organic Matter and Red Chili Yields

&#x0D; &#x0D; &#x0D; &#x0D; This study examines the impact of biochar and poschar fertilisers derived from livestock manure on red chilli yields and soil organic matter variations. The results demonstrated that the interaction between biochar and poschar substantially affected the fresh chilli pod weight per plant and organic matter content. Applying biochar and poschar organic fertilisers derived from animal manure substantially influenced plant height, fresh fruit weight per plant, and soil organic matter. The interaction between cow manure and poschar from poultry manure produced the highest yield of chilli fruit weight per plant, or an increase of 323.88% compared to the absence of biochar and poschar. The organic matter content of the soil underwent a significant change, with the highest organic matter content resulting from the interaction between chicken biochar and chicken poschar, which increased by 143,00% compared to the control group.&#x0D; &#x0D; &#x0D; &#x0D; &#x0D;

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.

Effects of Variation in Tamarix chinensis Plantations on Soil Microbial Community Composition in the Middle Yellow River Floodplain

Floodplains have important ecological and hydrological functions in terrestrial ecosystems, experience severe soil erosion, and are vulnerable to losing soil fertility. Tamarix chinensis Lour. plantation is the main vegetation restoration measure for maintaining soil quality in floodplains. Soil microorganisms are essential for driving biogeochemical cycling processes. However, the effects of sampling location and shrub patch size on soil microbial community composition remain unclear. In this study, we characterized changes in microbial structure, as well as the factors driving them, in inside- and outside-canopy soils of three patch sizes (small, medium, large) of T. chinensis plants in the middle Yellow River floodplain. Compared with the outside-canopy soils, inside-canopy had higher microbial phospholipid fatty acids (PLFAs), including fungi, bacteria, Gram-positive bacteria (GP), Gram-negative bacteria (GN), and arbuscular mycorrhizal fungi. The ratio of fungi to bacteria and GP to GN gradually decreased as shrub patch size increased. Differences between inside-canopy and outside-canopy soils in soil nutrients (organic matter, total nitrogen, and available phosphorus) and soil salt content increased by 59.73%, 40.75%, 34.41%, and 110.08% from small to large shrub patch size. Changes in microbial community composition were mainly driven by variation in soil organic matter, which accounted for 61.90% of the variation in inside-canopy soils. Resource islands could alter microbial community structure, and this effect was stronger when shrub patch size was large. The results indicated that T. chinensis plantations enhanced the soil nutrient contents (organic matter, total nitrogen, and available phosphorus) and elevated soil microbial biomass and changed microbial community composition; T. chinensis plantations might thus provide a suitable approach for restoring degraded floodplain ecosystems.

High-Resolution Mapping of Soil Organic Matter at the Field Scale Using UAV Hyperspectral Images with a Small Calibration Dataset

The rapid acquisition of high-resolution spatial distribution of soil organic matter (SOM) at the field scale is essential for precision agriculture. The UAV imaging hyperspectral technology, with its high spatial resolution and timeliness, can fill the research gap between ground-based monitoring and remote sensing. This study aimed to test the feasibility of using UAV hyperspectral data (400–1000 nm) with a small-sized calibration sample set for mapping SOM at a 1 m resolution in typical low-relief black soil areas of Northeast China. The experiment was conducted in an approximately 20 ha field. For calibration, 20 samples were collected using a 100 × 100 m grid sampling strategy, while 20 samples were randomly collected for independent validation. UAV captured hyperspectral images with a spatial resolution of 0.05 × 0.05 m. The extracted spectra within every 1 × 1 m were then averaged to represent the spectra of that grid; this procedure was also performed across the whole field. Upon applying various spectral pretreatments, including absorbance conversion, multiple scattering correction, Savitzky–Golay smoothing filtering, and first-order differentiation, the absolute maximum values of the correlation coefficients of the spectra for SOM increased from 0.41 to 0.58. Importance analysis from the optimal random forest (RF) model showed that the characterized bands of SOM were located in the 450–600 and 750–900 nm regions. When the RF model was used, the UAV hyperspectra data (UAV-RF) were able to successfully predict SOM, with an R2 of 0.53 and RMSE of 1.48 g kg−1. The prediction accuracy was then compared with that obtained using ordinary kriging (OK) and the RF model based on proximal sensing (PS-RF) with the same number of calibration samples. However, the OK method failed to predict the SOM accuracy (RMSE = 2.17 g kg−1; R2 = 0.02) due to a low sampling density. The semi-covariance function was unable to describe the spatial variability of SOM effectively. When the sampling density was increased to 50 × 50 m, OK successfully predicted SOM, with RMSE = 1.37 g kg−1 and R2 = 0.59, and its results were comparable to those of UAV-RF. The prediction accuracy of PS-RF was generally consistent with that of UAV-RF, with RMSE values of 1.41 g kg−1 and 1.48 g kg−1 and R2 values of 0.57 and 0.53, respectively, which indicated that SOM prediction based on UAV-RF is feasible. Additionally, compared with the PS platforms, the UAV hyperspectral technology could simultaneously provide spectral information of tens or even hundreds of continuous bands and spatial information at the same time. This study provides a reference for further research and development of UAV hyperspectral techniques for fine-scale SOM mapping using a small number of samples.

Geospatial and temporal assessment of the variability of soil organic matter and electrical conductivity in irrigated semi-arid area

ABSTRACT A good understanding of the dynamics of soil organic matter and electrical conductivity, in space and time dimension, is crucial for better agricultural and environmental management. This understanding is more required especially in an irrigated semi-arid region such as Doukkala plain in western of Morocco. To assess the status and dynamics of these two soils parameters in such area, in this study, we collected 100 soil samples at two depths (0–20 and 20–40) and over two successive years (2017 and 2018) using a GPS. Then we developed eight ordinary kriging models to produce digital soil maps. The findings indicate that the low content of organic matter in the soils investigated may have a negative impact on soil quality and productivity. Nonetheless, its increasing in the second year could be a sign of future improvement. Salinization of soils, on the other hand, is a continual process that occurs from year to year, whether in the superficial layer or the layer below. This alarming situation must be seriously taken into account in the plans and strategies of decision makers involved in agricultural and environmental policy in order to reduce soil degradation and preserve its quality and productive potential for sustainable food security.

Multi-Scale Integration and Distribution of Soil Organic Matter Spatial Variation in a Coal–Grain Compound Area

Soil organic matter (SOM) scale effects are critical for crop growth and food security, especially in coal–grain complexes. However, few studies describe the spatial variation in SOM and its influencing factors at different sampling scales. Here, geostatistical theory and mathematical statistical methods were adopted to analyze the spatial variation characteristics of and structural differences in SOM in the coal mining subsidence area at Zhaogu No. 2 Mine at different sampling scales. The results showed that SOM varied spatially at large, medium, and small scales, and the coefficients of variation were 28.07%, 14.93%, and 14.31%, respectively, which are moderate values. The characteristic functions of the SOM content at different sampling scales differed, and the spatial structure scale effect was obvious. The spatial distribution of the SOM content fitted by the multiscale fitting model method was generally the same as the spatial distribution law of the SOM content fitted by the single scale kriging interpolation method; however, in terms of the detailed expression and spatial distribution of small-scale SOM content, the fitting model method was more accurate, and the accuracy increased by 36%. At the different sampling scales, sample size and soil type had specific effects on the SOM spatial distribution. These results provide research concepts and technical countermeasures for improving food security and the ecological environment in the coal–grain complex and help ensure sustainable agricultural lands.

Digital morphometrics and genesis of soils with buried horizons and lithological discontinuities in southeastern Brazil

Soil morphometrics aims to measure attributes observed in soil profiles. The use of digital technologies in the morphological study of soil profiles has yet to be further explored by the scientific community. Particularly, soils with buried horizons, which are common in lowlands and floodplains throughout the world, have never been examined using digital morphometrics. This study aimed to examine the morphology and genesis of three soil profiles with buried horizons in Brazil using digital soil morphometrics combined with wet-chemistry and mineralogical analyses (including X-ray diffraction – XRD). Digital analyses used three proximal sensors – portable X-ray fluorescence spectrometry (pXRF), visible near-infrared (Vis-NIR) and mid-infrared (MIR) diffuse reflectance spectrometry. The soils had buried horizons and were classified as Typic Udifolists (TU), Typic Dystrustepts (TD) and Typic Endoaquents (TE). Each soil profile was sampled in a 15 × 15 cm grid, with 40 samples per profile. All sensors identified the buried soil horizons. Vis-NIR and MIR identified the presence of several key minerals and variations in soil organic matter (SOM) that were not observed in the whole soil pits. PXRF captured discontinuities in the parent material, not only between deposited and buried horizons, but also within horizons. The deposited material above TU and TE was derived from neighboring Fe-rich Oxisols and Ultisols, as shown by high Fe content and absorption features of Fe oxides. Sensor analyses were quick and environmentally friendly. Proximal sensor data enhanced morphological assessment of soil profiles, and provided insights otherwise very difficult or even impossible with only traditional and visual methods.

Synergetic use of DEM derivatives, Sentinel-1 and Sentinel-2 data for mapping soil properties of a sloped cropland based on a two-step ensemble learning method

Understanding the spatial variability of soil organic matter (SOM), soil total nitrogen (STN), soil total phosphorus (STP), and soil total potassium (STK) is important to support site-specific agronomic management, food production, and climate change adaptation. High-resolution remote sensing imageries have emerged as an innovative solution to investigate the spatial variation in agricultural soils with machine learning (ML) algorithms. However, the predictive power of the individual and combined effects of Sentinel-1 (S1) synthetic aperture radar (SAR) and Sentinel-2 (S2) multispectral images for mapping soil properties, especially STN, STP, and STK, have rarely been investigated. Moreover, single ML model may achieve unstable performance for predicting multiple soil properties due to strong spatial heterogeneity. This study explored the combine use of S1, S2, and DEM derivatives to map SOM, STN, STP, and STK content of a sloped cropland of northeastern China. A two-step method with a weighted sum of four ML models was proposed to improve the accuracy and robustness in predicting multiple soil properties. Our results showed that single ML model has various performance in predicting the four soil properties. The optimal ML models could explain approximately 56 %, 53 %, 56 % and 37 % of the variability of SOM, STN, STP, and STK, respectively. Using the weights estimated through a 10-fold cross-validation procedure, the two-step ensemble learning model was retrained and showed more robust performance than the four ML models, in which the prediction accuracy was improved by 2.38 %, 1.40 %, 3.52 %, and 3.29 % for SOM, STN, STP, and STK, respectively. Our results also showed that the optical S2 derived features, especially the two S2 short-wave infrared bands, enhanced vegetation index, and soil adjusted vegetation index, were more important for soil property prediction than S1 data and DEM derivatives. Compared with individual sensor, a combination of S1 and S2 data yielded more accurate predictions of STN and STP but not for SOM and STK. The results of this study highlight the potential of high-resolution S1 and S2 data and the two-step method for soil property prediction at farmland scale.

Spatial Variability of the Organic Matter in the Soil in Cassava Cultivation Under Differentiated Management

The management of soil organic matter (SOM) is fundamental in agriculture for soil conservation and crop yields. However, in addition the soil being dynamics, it is heterogeneous, therefore, understanding the spatial variability of SOM is essential. The main objective of this study was to evaluate the spatial variability of SOM in a cassava cultivation under different management, seeking to classify its spatial dependence by geostatistics. A filed experiment was conducted out on soil classified as oxisol with four different management systems: irrigation (micro sprinkler, drip and no irrigation), spacing (1.0 &amp;times; 0.8 m, 1.0 &amp;times; 1.0 m and 1.0 &amp;times; 1.5 m), weed control (manual control and no control) and acidity correction (limestone and withouth limestone), totaling 36 experimental plots. To determine the SOM, the wet oxidation method was used, and the semivariograms were generated by the GS+&amp;reg; software. The effect of the different management systems on the spatial variability of the SOM was evaluated at a depth of 0.0-0.2 m. The theoretical semivariogram model that best fitted the study was the Gaussian model, with a well-defined level, also expressing the condition of data stationarity. The spatial dependence was classified as strong and, through the thematic map generated from the kriging, it was possible to observe the variability in the SOM content for different management zones. The use of geostatistics techniques provided important information for understanding the spatial distribution of soil organic matter.

Effects of Long-Term Straw Return and Environmental Factors on the Spatiotemporal Variability of Soil Organic Matter in the Black Soil Region: A Case Study

Exploring the effects of straw return and environmental factors on the spatiotemporal variation of soil organic matter (SOM) in black soil regions is essential for soil carbon sequestration research. However, studies seldom quantified the effects of long-term straw return on a long-term SOM variation at a regional scale in typical black soil areas. The case was conducted in one of the three major black soil regions in the Northern Hemisphere, where the straw return policy has been implemented for a long time. The study obtained the SOM spatial distribution in 2007, 2009, 2012, 2015, and 2018 with approximately 9000 samples and analyzed the effects of soil types, texture, elevation, and human management on the spatiotemporal variation. The results indicated that from the 1980s to 2007, before the straw return policy implementation, the mean SOM content decreased from 24.38 g kg−1 to 18.94 g kg−1. In contrast, the mean SOM content gradually increased from 2007 to 2018 after implementing straw return practices. In addition, the area of SOM within 20–30 g kg−1 increased gradually, with 32.2%, 40.5%, 50.2%, 49.4%, and 60.5% in 2007, 2009, 2012, 2015, and 2018, respectively. Surprisingly, the SOM within 30–40 g kg−1 emerged in 2018. The results indicated that returning straw to the field might promote SOM accumulation. However, the SOM contents in Phaezems (19.25–21.82 g kg−1) were lower than that in natural Phaezems (40–60 g kg−1), indicating severe degradation. The clay content positively correlated to SOM and was a major explanatory variable for the response of SOM to straw return. Straw return practices are promising measures in the black soil region and are worth exploring more effective approaches to allow straw return to play a better role.