The document compares pore characteristics of soil aggregates from conventional row-crop agriculture (CT) and native succession vegetation (NS). X-ray microtomography was used to scan aggregates from both land uses. Analysis found that CT aggregates had significantly greater porosity and more complex pore networks than NS aggregates. However, NS aggregates had larger pores and pore networks that were more widely spaced, indicating greater variability in accessibility for air, water, and microbes.

1. Comparison of Intra-Aggregate Pore Characteristics From Soil in Conventional Row-Crop Agriculture and Native Succession Vegetation W. Wang 1 , A.N. Kravchenko 1 , K. Ananyeva 1 , A.J. M. Smucker 1 , H. Chun 1 and M. L. Rivers 2 1 Department of Crop & Soil Sciences, Michigan State University, East Lansing, MI 48824 2 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 References Image 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. Notations Burn 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. Acknowledgement This 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). Maximum burn number a b Porosity a b Normalized number of nodal pores a b Largest nodal pore a b Normalized number of throats a b Normalized number of MA paths a b Fig 1. Schematic representation of data processing T 1 T 2 Pore characteristics : porosity, burn number, Medial axis, throats, nodal pores throats Nodal pore Medial axis (MA) 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 .