Spatial Distribution of Soil Organic Matter Using Geostatistics: A Key Indicator to Assess Soil Degradation Status in Central Italy

Pedosphere ◽  
2012 ◽  
Vol 22 (2) ◽  
pp. 230-242 ◽  
Author(s):  
A. MARCHETTI ◽  
C. PICCINI ◽  
R. FRANCAVIGLIA ◽  
L. MABIT
2020 ◽  
Author(s):  
Holger Pagel ◽  
Björn Kriesche ◽  
Marie Uksa ◽  
Christian Poll ◽  
Ellen Kandeler ◽  
...  

<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>


2010 ◽  
Vol 56 (No. 2) ◽  
pp. 87-97 ◽  
Author(s):  
X.B. Liu ◽  
X.Y. Zhang ◽  
Y.X. Wang ◽  
Y.Y. Sui ◽  
S.L. Zhang ◽  
...  

Soil degradation that results from erosion, losses of organic matter and nutrients, or soil compaction are of great concern in every agricultural region of the world. The control of soil erosion and loss of organic matter has been proposed as critical to agricultural and environmental sustainability of Northeast China. This region is bread basket of China where the fertile and productive soils, Mollisols (also called Black soils), are primarily distributed. In this paper, we introduce the importance of Northeast China’s grain production to China, and describe the changes of sown acreage and grain production in past decades. This paper also summarizes the distribution, area and intensity of water erosion, changes in the number of gullies and gully density, thickness of top soil layer, soil organic matter content, bulk density, field water holding capacity, and infiltration rates; the number of soil microorganism and main enzyme activities from soil erosion in the region are also summarized. The moderately and severely water-eroded area accounted for 31.4% and 7.9% of the total, and annual declining rate is 1.8%. Erosion rate is 1.24–2.41 mm/year, and soil loss in 1°, 5° and 15° sloping farmlands is 3 t/ha/year, 78 t/ha/year and 220.5 t/ha/year, respectively. SOC content of uncultivated soil was nearly twice that of soil with a 50-year cultivation history, and the average annual declining rate of soil organic matter was 0.5%. Proper adoption of crop rotation can increase or maintain the quantity and quality of soil organic matter, and improve soil chemical and physical properties. Proposed strategies for erosion control, in particular how tillage management, terraces and strip cultivation, or soil amendments contribute to maintain or restore the productivity of severely eroded farmland, are discussed in the context of agricultural sustainability with an emphasis on the Chinese Mollisols.


2022 ◽  
Author(s):  
Xumeng Zhang ◽  
Wuping Zhang ◽  
Mingjing Huang ◽  
Li Gao ◽  
Lei Qiao ◽  
...  

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.


2020 ◽  
Vol 79 (8) ◽  
pp. 4005-4020 ◽  
Author(s):  
Elena Benedetta Masi ◽  
Gabriele Bicocchi ◽  
Filippo Catani

Abstract Soil organic matter (SOM) represents a main fraction of superficial soil characterized by a mechanical-hydrological behaviour different from that of the inorganic fractions. In this study, a method to measure the SOM content was applied to 27 selected sites in Tuscany (central Italy) characterized by the presence of soil types common in the region: cambisols and regosols. The method included the contribution from root fragments, which is a fraction often neglected or underestimated in measurements, in the overall estimate of the SOM content. The retrieved SOM contents were analysed considering the vegetation cover at the sites and the selected attributes of geological interest, such as geotechnical parameters and the mineralogical composition of the soils. The SOM normalized to the bulk samples ranges between 1.8 and 8.9% by weight, with the highest values of the SOM content being associated with vegetation cover classes of forest and woodlands without shrubs. The SOM values showed close relationships with the abundance of the finer fractions (silt and clay) of the soil samples, and considering the relations with geotechnical properties, moderate correlations were found with the plasticity index, unit weight and effective friction angle, overall demonstrating the importance of considering SOM when the geotechnical and hydrological properties of soils are evaluated.


Land ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 349
Author(s):  
Chiara Piccini ◽  
Rosa Francaviglia ◽  
Alessandro Marchetti

Organic matter, an important component of healthy soils, may be used as an indicator in sustainability assessments. Managing soil carbon storage can foster agricultural productivity and environmental quality, reducing the severity and costs of natural phenomena. Thus, accurately estimating the spatial variability of soil organic matter (SOM) is crucial for sustainable soil management when planning agro-environmental measures at the regional level. SOM variability is very large in Italy, and soil organic carbon (SOC) surveys considering such variability are difficult and onerous. The study concerns the Abruzzo Region (about 10,800 km2), in Central Italy, where data from 1753 soil profiles were available, together with a Digital Elevation Model (DEM) and Landsat images. Some morphometric parameters and spectral indices with a significant degree of correlation with measured data were used as predictors for regression-kriging (RK) application. Estimated map of SOC stocks, and of SOM related to USDA (United States Department of Agriculture) texture—an additional indicator of soil quality—were produced with a satisfactory level of accuracy. Results showed that SOC stocks and SOM concentrations in relation to texture were lower in the hilly area along the shoreline, pointing out the need to improve soil management to guarantee agricultural land sustainability.


2021 ◽  
Author(s):  
Carla S. S. Ferreira ◽  
Samaneh Seifollahi-Aghmiuni ◽  
Georgia Destouni ◽  
Marijana Solomun ◽  
Navid Ghajarnia ◽  
...  

<p>Soil supports life on Earth and provides several goods and services of essence for human wellbeing. Over the last century, however, intensified human activities and unsustainable management practices, along with ongoing climate change, have been degrading soils’ natural capital, pushing it towards possible critical limits for its ability to provide essential ecosystem services. Soil degradation is characterized by negative changes in soil health status that may lead to partial or total loss of productivity and overall capacity to support human societies, e.g., against increasing climate risks. Such degradation leads to environmental, social and economic losses, which may in turn trigger land abandonment and desertification. In particular, the Mediterranean region has been identified as one of the most vulnerable and severely affected European regions by soil degradation, where the actual extent and context of the problem is not yet well understood. This study provides an overview of current knowledge about the status of soil degradation and its main drivers and processes in the European Mediterranean region, based on comprehensive literature review. In the Mediterranean region, 34% of the land area is subject to ‘very high sensitivity’ or ‘high sensitivity’ to desertification, and risk of desertification applies to over more than 65% of the territory of some countries, such as Spain and Cyprus (IPCC, 2019). The major degradation processes are: (i) soil erosion, due to very high erosion rates (>2 t/ha); (ii) loss of soil organic matter, due to high mineralization rates while the region is already characterized by low or very low soil organic matter (<2%); and (iii) soil and water salinisation, due to groundwater abstraction and sea water intrusion. However, additional physical, chemical and biological degradation processes, such as soil sealing and compaction, contamination, and loss of biodiversity, are also of great concern. Some of the degradation processes, such as soil erosion, have been extensively investigated and their spatial extent is relatively well described. Other processes, however, such as soil biodiversity, are poorly investigated and have limited data availability. In general, a lack of systematic inventories of soil degradation status limits the overall knowledge base and impairs understanding of the spatial and temporal dimensions of the problem. In terms of drivers, Mediterranean soil degradation has mainly been driven by increasing population, particularly in coastal areas, and its concentration in urban areas (and consequent abandonment of rural areas), as well as by land-use changes and intensification of socio-economic activities (e.g. agriculture and tourism). Additionally, climate change, with increasing extent and severity of extreme events (droughts, floods, wildfires), may also be a key degradation driver in this region. Improved information on soil degradation status (including spatio-temporal extent and severity) and enhanced knowledge of degradation drivers, processes and socio-economic, ecological, and biodiversity impacts are needed to better support regional soil management, policy, and decision making. Science and evidence based improvements of soil resource governance and management can enhance soil resilience to regional and global changes, and support the region to achieve related Sustainable Development Goals and the Land Degradation Neutrality targets.</p>


Solid Earth ◽  
2017 ◽  
Vol 8 (4) ◽  
pp. 827-843 ◽  
Author(s):  
Sunday Adenrele Adeniyi ◽  
Willem Petrus de Clercq ◽  
Adriaan van Niekerk

Abstract. Cocoa agroecosystems are a major land-use type in the tropical rainforest belt of West Africa, reportedly associated with several ecological changes, including soil degradation. This study aims to develop a composite soil degradation assessment index (CSDI) for determining the degradation level of cocoa soils under smallholder agroecosystems of southwestern Nigeria. Plots where natural forests have been converted to cocoa agroecosystems of ages 1–10, 11–40, and 41–80 years, respectively representing young cocoa plantations (YCPs), mature cocoa plantations (MCPs), and senescent cocoa plantations (SCPs), were identified to represent the biological cycle of the cocoa tree. Soil samples were collected at a depth of 0 to 20 cm in each plot and analysed in terms of their physical, chemical, and biological properties. Factor analysis of soil data revealed four major interacting soil degradation processes: decline in soil nutrients, loss of soil organic matter, increase in soil acidity, and the breakdown of soil textural characteristics over time. These processes were represented by eight soil properties (extractable zinc, silt, soil organic matter (SOM), cation exchange capacity (CEC), available phosphorus, total porosity, pH, and clay content). These soil properties were subjected to forward stepwise discriminant analysis (STEPDA), and the result showed that four soil properties (extractable zinc, cation exchange capacity, SOM, and clay content) are the most useful in separating the studied soils into YCP, MCP, and SCP. In this way, we have sufficiently eliminated redundancy in the final selection of soil degradation indicators. Based on these four soil parameters, a CSDI was developed and used to classify selected cocoa soils into three different classes of degradation. The results revealed that 65 % of the selected cocoa farms are moderately degraded, while 18 % have a high degradation status. The numerical value of the CSDI as an objective index of soil degradation under cocoa agroecosystems was statistically validated. The results of this study reveal that soil management should promote activities that help to increase organic matter and reduce Zn deficiency over the cocoa growth cycle. Finally, the newly developed CSDI can provide an early warning of soil degradation processes and help farmers and extension officers to implement rehabilitation practices on degraded cocoa soils.


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