Patterns and Sources of Spatial Heterogeneity in Soil Matrix From Contrasting Long Term Management Practices

With the advent of computed microtomography (μCT), in situ 3D visualization of soil at micron scale became easily achievable. However, most μCT-based research has focused on visualization and quantification of soil pores, roots, and particulate organic matter (POM), while little effort has been put...

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Main Authors: Michelle Y. Quigley, Mark L. Rivers, Alexandra N. Kravchenko
Format: Article
Language:English
Published: Frontiers Media S.A. 2018-05-01
Series:Frontiers in Environmental Science
Subjects:
Online Access:https://www.frontiersin.org/article/10.3389/fenvs.2018.00028/full
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spelling doaj-2e62eeb01b7948409e913251f735708b2020-11-25T01:59:29ZengFrontiers Media S.A.Frontiers in Environmental Science2296-665X2018-05-01610.3389/fenvs.2018.00028359719Patterns and Sources of Spatial Heterogeneity in Soil Matrix From Contrasting Long Term Management PracticesMichelle Y. Quigley0Mark L. Rivers1Alexandra N. Kravchenko2Alexandra N. Kravchenko3Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, United StatesArgonne National Lab, Center for Advanced Radiation Sources, The University of Chicago, Chicago, IL, United StatesDepartment of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, United StatesDepartment of Agricultural Soil Science, University of Göttingen, Göttingen, GermanyWith the advent of computed microtomography (μCT), in situ 3D visualization of soil at micron scale became easily achievable. However, most μCT-based research has focused on visualization and quantification of soil pores, roots, and particulate organic matter (POM), while little effort has been put in exploring the soil matrix itself. This study aims to characterize spatial heterogeneity of soil matrix in macroaggregates from three differing long term managements: conventionally managed and biologically based row-crop agricultural systems and primary successional unmanaged system, and explore the utility of using grayscale gradients as a proxy of soil organic matter (SOM). To determine spatial characteristics of the soil matrix, we completed a geostatistical analysis of the aggregate matrix. It demonstrated that, while the treatments had the same range of spatial correlation, there was much greater overall variability in soil from the biologically based system. Since soil from both managements have the same mineralogy and texture, we hypothesized that greater variability is due to differences in SOM distributions, driven by spatial distribution patterns of soil pores. To test this hypothesis, we applied osmium (Os) staining to intact micro-cores from the biologically based management, and examined Os staining gradients every 4 μm from 26 to 213 μm from pores of biological or non-biological origin. Biological pores had the highest SOM levels adjacent to the pore, which receded to background levels at distances of 100–130 μm. Non-biological pores had lower SOM levels adjacent to the pores and returned to background levels at distances of 30–50 μm. This indicates that some of the spatial heterogeneity within the soil matrix can be ascribed to SOM distribution patterns as controlled by pore origins and distributions. Lastly, to determine if the grayscale values could be used as a proxy for SOM levels, gradients of grayscale values from biological and non-biological pores were compared with the Os gradients. Grayscale gradients matched Os gradients for biological pores, but not non-biological pores due to an image processing artifact. Grayscale gradients would, therefore, be a good proxy for SOM gradients near biological origin pores, while their use for non-biological pores should be conducted with caution.https://www.frontiersin.org/article/10.3389/fenvs.2018.00028/fullmicrotomographysoil organic mattergeostatisticsspatial variabilityparticulate organic matter
collection DOAJ
language English
format Article
sources DOAJ
author Michelle Y. Quigley
Mark L. Rivers
Alexandra N. Kravchenko
Alexandra N. Kravchenko
spellingShingle Michelle Y. Quigley
Mark L. Rivers
Alexandra N. Kravchenko
Alexandra N. Kravchenko
Patterns and Sources of Spatial Heterogeneity in Soil Matrix From Contrasting Long Term Management Practices
Frontiers in Environmental Science
microtomography
soil organic matter
geostatistics
spatial variability
particulate organic matter
author_facet Michelle Y. Quigley
Mark L. Rivers
Alexandra N. Kravchenko
Alexandra N. Kravchenko
author_sort Michelle Y. Quigley
title Patterns and Sources of Spatial Heterogeneity in Soil Matrix From Contrasting Long Term Management Practices
title_short Patterns and Sources of Spatial Heterogeneity in Soil Matrix From Contrasting Long Term Management Practices
title_full Patterns and Sources of Spatial Heterogeneity in Soil Matrix From Contrasting Long Term Management Practices
title_fullStr Patterns and Sources of Spatial Heterogeneity in Soil Matrix From Contrasting Long Term Management Practices
title_full_unstemmed Patterns and Sources of Spatial Heterogeneity in Soil Matrix From Contrasting Long Term Management Practices
title_sort patterns and sources of spatial heterogeneity in soil matrix from contrasting long term management practices
publisher Frontiers Media S.A.
series Frontiers in Environmental Science
issn 2296-665X
publishDate 2018-05-01
description With the advent of computed microtomography (μCT), in situ 3D visualization of soil at micron scale became easily achievable. However, most μCT-based research has focused on visualization and quantification of soil pores, roots, and particulate organic matter (POM), while little effort has been put in exploring the soil matrix itself. This study aims to characterize spatial heterogeneity of soil matrix in macroaggregates from three differing long term managements: conventionally managed and biologically based row-crop agricultural systems and primary successional unmanaged system, and explore the utility of using grayscale gradients as a proxy of soil organic matter (SOM). To determine spatial characteristics of the soil matrix, we completed a geostatistical analysis of the aggregate matrix. It demonstrated that, while the treatments had the same range of spatial correlation, there was much greater overall variability in soil from the biologically based system. Since soil from both managements have the same mineralogy and texture, we hypothesized that greater variability is due to differences in SOM distributions, driven by spatial distribution patterns of soil pores. To test this hypothesis, we applied osmium (Os) staining to intact micro-cores from the biologically based management, and examined Os staining gradients every 4 μm from 26 to 213 μm from pores of biological or non-biological origin. Biological pores had the highest SOM levels adjacent to the pore, which receded to background levels at distances of 100–130 μm. Non-biological pores had lower SOM levels adjacent to the pores and returned to background levels at distances of 30–50 μm. This indicates that some of the spatial heterogeneity within the soil matrix can be ascribed to SOM distribution patterns as controlled by pore origins and distributions. Lastly, to determine if the grayscale values could be used as a proxy for SOM levels, gradients of grayscale values from biological and non-biological pores were compared with the Os gradients. Grayscale gradients matched Os gradients for biological pores, but not non-biological pores due to an image processing artifact. Grayscale gradients would, therefore, be a good proxy for SOM gradients near biological origin pores, while their use for non-biological pores should be conducted with caution.
topic microtomography
soil organic matter
geostatistics
spatial variability
particulate organic matter
url https://www.frontiersin.org/article/10.3389/fenvs.2018.00028/full
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