Predicting spatial distribution of soil organic matter using regression approaches at the regional scale (Eastern Azerbaijan, Iran)

2021 ◽  
Vol 193 (9) ◽  
Author(s):  
Golnaz Ebrahimzadeh ◽  
Nafiseh Yaghmaeian Mahabadi ◽  
Kamal Khosravi Aqdam ◽  
Farrokh Asadzadeh
Keyword(s):  
Organic Matter ◽  
Spatial Distribution ◽  
Soil Organic Matter ◽  
Regional Scale

scholarly journals Spatial control of carbon dynamics in soil by microbial decomposer communities

2020 ◽  
Author(s):  
Holger Pagel ◽  
Björn Kriesche ◽  
Marie Uksa ◽  
Christian Poll ◽  
Ellen Kandeler ◽  
...  
Keyword(s):  
Organic Matter ◽  
Spatial Distribution ◽  
Soil Organic Matter ◽  
Distribution Patterns ◽  
Microbial Succession ◽  
Thin Sections ◽  
Spatial Control ◽  
Soil Organic Matter Turnover ◽  
Microbial Decomposer ◽  
Modelling Approach

<p>Trait-based models have improved the understanding and prediction of soil organic matter dynamics in terrestrial ecosystems. Microscopic observations and pore scale models are now increasingly used to quantify and elucidate the effects of soil heterogeneity on microbial processes. Combining both approaches provides a promising way to accurately capture spatial microbial-physicochemical interactions and to predict overall system behavior. The present study aims to quantify controls on carbon (C) turnover in soil due to the mm-scale spatial distribution of microbial decomposer communities in soil. A new spatially explicit trait-based model (SpatC) has been developed that captures the combined dynamics of microbes and soil organic matter (SOM) by taking into account microbial life-history traits and SOM accessibility. Samples of spatial distributions of microbes at µm-scale resolution were generated using a spatial statistical model based on Log Gaussian Cox Processes which was originally used to analyze distributions of bacterial cells in soil thin sections. These µm-scale distribution patterns were then aggregated to derive distributions of microorganisms at mm-scale. We performed Monte-Carlo simulations with microbial distributions that differ in mm-scale spatial heterogeneity and functional community composition (oligotrophs, copiotrophs and copiotrophic cheaters). Our modelling approach revealed that the spatial distribution of soil microorganisms triggers spatiotemporal patterns of C utilization and microbial succession. Only strong spatial clustering of decomposer communities induces a diffusion limitation of the substrate supply on the microhabitat scale, which significantly reduces the total decomposition of C compounds and the overall microbial growth. However, decomposer communities act as functionally redundant microbial guilds with only slight changes in C utilization. The combined statistical and process-based modelling approach derives distribution patterns of microorganisms at the mm-scale from microbial biogeography at microhabitat scale (µm) and quantifies the emergent macroscopic (cm) microbial and C dynamics. Thus, it effectively links observable process dynamics to the spatial control by microbial communities. Our study highlights a powerful approach that can provide further insights into the biological control of soil organic matter turnover.</p>

scholarly journals Comparison of Different Spatial Regression Methods for Predicting Organic Matter Content in Soil

2022 ◽  
Author(s):  
Xumeng Zhang ◽  
Wuping Zhang ◽  
Mingjing Huang ◽  
Li Gao ◽  
Lei Qiao ◽  
...  
Keyword(s):  
Organic Matter ◽  
Spatial Distribution ◽  
Soil Organic Matter ◽  
Land Use Planning ◽  
Prediction Accuracy ◽  
Meteorological Data ◽  
Organic Matter Content ◽  
Spatial Prediction ◽  
Matter Content ◽  
Soil Organic Matter Content

Abstract Dynamic changes in soil organic matter content affects the sustainable supply of soil water and fertilizer and impacts the stability of soil ecological function. Understanding the spatial distribution characteristics of soil organic matter will help deepen our understanding of the differences in soil organic matter content, soil formation law; such understanding would be useful for rational land use planning. Taking terrain data, meteorological data, and remote sensing data as auxiliary variables and the ordinary Kriging (OK) method as a control, this study compares the spatial prediction accuracies and mapping effects of various models (MLR, RK, GWR, GWRK, MGWR, and MGWRK) on soil organic matter. Our results show that the spatial distribution trend of soil organic matter predicted by each model is similar, but the prediction of composite models can reflect more mapping details than that of unitary models. The OK method can provide better support for spatial prediction when the sampling points are dense; however, the local models are superior in dealing with spatial non-stationarity. Notably, the MGWR model is superior to the GWR model, but the MGWRK model is inferior to the GWRK model. As a new method, the prediction accuracy of MGWRK reached 47.72% for the OK and RK methods and 40.08% for the GWRK method. The GWRK method achieved a better prediction accuracy. The influence mechanism of soil organic matter is complex, but the MGWR model more clearly reveals the complex nonlinear relationship between soil organic matter content and factors influencing it. This research can provide reference methods and mapping technical support to improve the spatial prediction accuracy of soil organic matter.

Three-Dimensional Spatial Simulation and Distribution Characteristics of Soil Organic Matter in Coal Mining Subsidence Area

2020 ◽  
Vol 980 ◽  
pp. 437-448
Author(s):  
Hui Juan Zhang ◽  
Shou Chen Ma ◽  
Wen Kai Liu ◽  
He Bing Zhang ◽  
Song He Yuan
Keyword(s):  
Organic Matter ◽  
Spatial Distribution ◽  
Soil Organic Matter ◽  
Stochastic Simulation ◽  
Interpolation Method ◽  
Soil Depth ◽  
Simulation Method ◽  
Kriging Interpolation ◽  
Kriging Interpolation Method ◽  
Middle Slope

Underground mining has caused drastic disturbances to regional ecosystems and soil nutrients. Understanding the 3D spatial distribution of soil organic matter in coal arable land is crucial for agricultural production and environmental management. However, little research has been done on the three-dimensional modeling of soil organic matter. In this study, 3D kriging interpolation method and 3D stochastic simulation method were used to develop the 3D model of soil organic matter , and the root-mean-square error (RMSE) and mean error (ME) were used as evaluation indexes to compare the simulation accuracy of the two methods. Results showed that the spatial distribution of soil organic matter obtained by using 3D kriging interpolation method is relatively smooth, which reduce the difference of spatial data; while the spatial distribution of soil organic matter obtained by using 3D stochastic simulation method is relatively discrete and highlights the volatility of spatial distribution of raw data, the RMSE obtained by 3D kriging interpolation method and 3D stochastic simulation method respectively is 2.7711 g/kg and 1.8369 g/kg. The prediction accuracy of organic matter interpolation obtained by 3D stochastic simulation method is higher than that by 3D kriging interpolation method; so the 3D stochastic simulation method can reflect the spatial distribution characteristics of soil organic matter more realistically, and more suitable for 3D modeling of soil organic matter. According to the 3D modeling of soil organic matter, the content of soil organic matter has obvious spatial difference in different soil depth（0-20 cm、20-40 cm、40-60 cm） and decreases with the increase of soil depth; The result also showed that the content of soil organic matter decreased rapidly from the upper slope to the middle slope, and gradually increased from the middle slope to the bottom, so the soil organic matter content was obviously lost in the middle slope. This result may provide useful data for land reclamation and ecological reconstruction in coal mining subsidence area.

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