Comparison of Intra-Aggregate Pore Characteristics From Soil in Conventional Row-Crop Agriculture and Native Succession Vegetation

W. Wang 1, A.N. Kravchenko1, K. Ananyeva1, A.J. M. Smucker1, H. Chun1 and M. L. Rivers2

1Department of Crop & Soil Sciences, Michigan State University, East Lansing, MI 488242 13-BM-D, APS/CARS-CAT, University of Chicago, c/o Argonne National Laboratory, Argonne, IL, 60439

Introduction ● Soil aggregates play an important role in many soil processes, including solution transport, nutrient storage, carbon sequestration and microbial activity. ● Aggregate contribution to soil processes depends on aggregate compositions and internal pore geometries. ● X-ray microtomography is a great tool that allows quantitative descriptions and comparisons of the aggregate structures and geometries.

In this study, we compared soil aggregates from two contrasting land uses: the conventional chisel plowed row-crop with standard chemical inputs (CT) and the native successional treatment (NS) located at LTER main site, Kellogg Biological Station, Michigan. For the past 20 years, CT was subjected to intensive agricultural management, while NS was undisturbed. We hypothesize that long-term differences in management led to significant difference in aggregate pore characteristics.

Material and Methods● 13 aggregates from CT and 12 aggregates from NS, 4mm-6mm in size, were used in the study.

● Aggregates were scanned on the bending magnet beam line, station 13-BM-D of the GeoSoilEnvironCARS (GSECARS) at the Advanced Photon Source, Argonne National Laboratory with 28 keV incident energy and a resolution of 14.6 microns. Raw data were converted to grey scale image data where grey scale value reflects the X-ray linear attenuation coefficients.

● Soil aggregates boundary in each image slice was identified by applying Gaussian smoothing filter in MATLAB (Image Processing Toolbox, 2003). The volume of the aggregates was recorded. Soil pore/solid classification was conducted using Indicator Kriging segmentation module in 3DMA-Rock software (Lindquist, 1999). The thresholds were determined by fitting mixed Gaussian distribution to the image grey-scale distribution. Fitting parameters were selected based on NU criterion (Wang, et. al, 2009). Pore characteristics were obtained from 3DMA-Rock. Schematic representation of the data processing is demonstrated in Fig. 1.

● Because of the highly demanding computational needs, all the calculations were conducted using the servers at High Performance Computing Center, Michigan State University (http://www.hpcc.msu.edu).

● One-way ANOVA was conducted for comparing various pore characteristics using PROC MIXED (SAS, 2001). The significance level was 0.05.

Comparison of pore characteristics between CT and NS

ReferencesImage Processing Toolbox, Users Guide 2003. Version 4. The Mathworks Inc., Natick, MA.SAS Institute. 2001. SAS user’s guide. Version 9.1. SAS Inst., Cary, NC.W. Wang, A.N. Kravchenko, A.J.M. Smucker and M.L. Rivers, 2009. A simulation study of image segmentation methods in microtomographic images of soil aggregates. Submitted to Geoderma.W. Oh, B. Lindquist. 1999. Image thesholding by indicator kriging. IEEE Transactions on Pattern Analysis and Machine Intelligence 21: 590-602.

NotationsBurn number: An integer distance from the pore voxel to the closest solid voxel in units of voxels

Medial axis (MA): A digitized curve which represents the skeletonization of the pore space by iteratively eroding the pore voxels

MA path: A connected set of MA voxels where each voxel connects to exactly two distinct voxels, except that it is only connected to one other MA voxel at the end of the path (Fig. 2.)

Throat: The local minimum cross-sectional surface which divides the pore space into pore bodies

Nodal pore: The adjoining pore bodies separated by throats

Conclusions ● CT aggregates have significantly greater porosity and more complex pore networks (MA paths, nodal pores, pore throats) than NS aggregates. Thus, the entire aggregate might have better air-water exchanges and greater accessibility to soil microorganisms. However, greater numbers of pores and fractures likely contribute to lower stability of soil aggregates from conventionally tilled soil. ● NS aggregates have pores with greater volume (largest nodal pore) and substantially wider (Max burn number) than those of CT aggregates. Many of the pores are of biological origin. Thus even though the overall air/water fluxes through the NS aggregates might be high, we can expect greater variability in how accessible to air/water/microbes are different parts of the NS aggregates.

AcknowledgementThis work was funded in part by the NSF LTER Program at KBS, Michigan Agricultural Experiment Station, and USDA-CSREES National Research Initiative: Water and Watersheds Program (Project 2008-35102-04567).

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Fig 1. Schematic representation of data processing

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Pore characteristics : porosity, burn number, Medial axis, throats, nodal pores

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Fig 2. An example of MA paths in a 3D object. MA paths are the connected sets of voxels in red intersected by voxels in blue.