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Abbreviations3Dthree‐dimensionalCCcover crop(s)CTcomputed tomographyEPReffective pore radiusμCTmicro‐computed tomographyNCCno cover cropsINTRODUCTIONLand degradation with conventional agriculture is as much as three times faster than with conservation agriculture and as much as 75 times faster than with native vegetation (Montgomery, 2007). In the United States, crop and pasture lands have declined by 22 and 14% from 1982 to 2007, and an additional 7% decline is predicted by 2030 (Nickerson et al., 2011). Furthermore, the use of large machinery and modern land management practices may deteriorate the physical, chemical, and biological health of soil, potentially decreasing crop productivity and environmental quality (Lal, 2015). Thus, land management to improve soil, water, and environmental quality must be further improved for sustainable food production (Udawatta et al., 2016).Cover crops (CC) may mitigate land degradation as they improve soil physical, chemical, and biological properties (Doran & Zeiss, 2000; Fageria et al., 2005) and overall soil quality by increasing soil organic matter (Ding et al., 2006). Cover crops also improve soil porosity by macropore creation through their growing root systems (Chen & Weli, 2010; Stirzaker & White, 1995; Williams & Weil, 2004). Cercioglu et al. (2018) reported a 50% greater macropore density in CC compared with no CC (NCC) management. Another study reported larger functional pores and more tubules in CC compared with NCC (Carof et al., 2007). Improved porosity with CC can help increase water infiltration, soil water storage (Daniel et al., 1999; Smart & Bradford, 1996), soil hydraulic properties, and water use efficiency (Johnson et al., 2016; Obalum et al., 2011). These beneficial effects are reflected by increased porosity and pore characteristics.Macropores promote water and air movement into soil. The size, shape, and orientation of macropores affect the flow rate and retention of water (Rasiah & Alymore, 1998; Udawatta et al., 2006; Van Noordwijk et al., 1991). Topology and size distribution of soil pores regulate soil organisms’ access to oxygen, water, and substrates; thus, the population size and structure of micro‐, meso‐, and macrofauna in soil (Young & Ritz, 2000). Lower macropore density and higher soil compaction can reduce root and shoot growth, mineral nutrient absorption, and rate of photosynthesis and break the balances of growth hormones, and this could lead to plant physiological disfunction (Kozlowski, 1999; Lipiec & Hatano, 2003). Plant roots help increase soil porosity, and a soil matrix with macropores of a greater diameter than roots facilitates undisturbed root growth (Lipiec & Hatano, 2003). Soil pore characteristics are strongly related to available soil water, soil microbial activities, and soil organic carbon stabilization (Arachchige et al., 2018; Peth et al., 2014; Udawatta, Gantzer, et al., 2008). Thus, it is important to increase soil porosity as well as quantify and understand soil pores’ geometrical distributional characteristics to postulate their relationships with other ecological functions.One of the methods for quantification of intact soil structures is the use of X‐ray computed tomography (CT) (Gantzer & Anderson, 2002). High‐resolution CT‐measured pore parameters provide actual physical pore geometrical locations, connectivity paths, and pore volume; thus, this is a good strategy for quantifying soil pore characteristics (Blunt et al., 2013; Cercioglu et al., 2018; Udawatta, Anderson, et al., 2008). Studies have shown that CT‐measured pore parameters under different management practices such as agroforestry buffers, native prairie, stiff‐stemmed grass hedges, grass buffers, conservation reserve program, and row crop management were well correlated with macropores estimated with water retention and saturated hydraulic conductivity (Rachman et al., 2005; Udawatta, Anderson, et al., 2008).However, studies quantifying CT‐measured geometrical pore distributional characteristics under CC and NCC management are limited. Knowledge of differences in pore characteristics between CC and NCC soils at micrometer resolution could be helpful to identify effects of CC use on soil physical characteristics. The objective of this study was to identify differences in pore characteristics between CC and NCC using CT‐measured geometrical pore distributional characteristics of the surface soil layer.Core IdeasCover crops improve soil pore volume measured by micro‐computed tomography (μCT).Cover crops improve soil physical characteristics.μCT‐measured imageJ analysis is a good method for soil geometrical pore characteristic identification.MATERIALS AND METHODSExperimental site and soil samplingThe study was conducted at the University of Missouri Bradford Research Center located in Columbia, MO (38°89′ N, 92°21′ W). The study design consisted of winter CC and summer corn (Zea mays L.)–soybean [Glycine max (L.) Merr.] rotation with no‐till management. The initial CC establishment occurred in fall 2011 and continued with CC planting every fall. Fall‐seeded CC started germinating in late January, and the CC was terminated with herbicides in May every year, before cash crop planting. The CC mixture consisted of cereal rye (Secale cereale L.), hairy vetch (Vicia villosa Roth.), Austrian winter pea (Pisum sativum L.), and crimson clover (Trifolium incarnatum L.) at rates of 67.2, 16.8, 22.4, and 11.2 kg ha−1, respectively. Corn was planted in summer 2017, and soybean was planted in 2018. Treatments for the study were NCC and CC management.The soil at the study site is Mexico silt loam (fine, smectitic, mesic Aeric Vertic Epiaqualf) soil. The annual precipitation was 1,083 mm with approximately 576 mm (53%) occurring from April through August. Mean annual air temperature was 18.2 °C, with an average monthly low of 3.6 °C in January and an average monthly high of 31.1 °C in July, whereas the average snowfall was 467 mm yr−1 (Udawatta et al., 2016).Sampling was carried out in May 2018 at the 50% flowering stage of CC. Intact soil cores of 28‐mm diameter and 65‐mm length were taken in three replicates per treatment in 1‐mm wall plastic cylinders. Before sampling, the sampling points were wet for 2 d, and plastic tubes were inserted smoothly to minimize soil compaction and physical damage to soil cores. The soil cores were labeled, placed in plastic bags, sealed, and stored in a refrigerator at 4 °C until analysis.Scanning and image analysisA Zeiss Xradia Versa 510 X‐ray microscope (XRM or micro‐computed tomography [μCT]) at University of Missouri Geological Sciences was used for three‐dimensional (3D) volume imaging of the samples. The X‐ray source operated at 80 kV and 7 W with minimum achievable voxel size of 70 nm was used to acquire images at 29‐μm resolution. Soil cores were placed on the scanner stage, and the position was adjusted with the red crosshair of the integrated visual light camera so that the X‐ray beam was perpendicular to the longitudinal axis. Rotation angle was set to 360°, and the exposure time was 1.0–1.5 s. Air was used as the source filter. The slice thickness was 29 μm, and approximately 2,250 images were acquired for each soil core.The Fiji distributed public domain software ImageJ2 was used for analyzing the scanned images (https://imagej.net/Fiji; Schindelin et al., 2012). The images from the top and bottom 7.25‐mm sections of each soil core were not used for analysis to remove artifacts created during sampling and preparation for scanning. Images were first converted to 8‐bit TIFF images to facilitate analysis (Lee et al., 2008), and the scale was set to 28‐mm diameter, which is the actual diameter of a soil core using “Set scale module” under Analyze tool in ImageJ. A 450‐mm2 circular region of interest was selected for image analysis, and outer area was removed to reduce artifacts and beam hardening effects. Within a core, sections at depths of 7.25–27.25 mm and 37.25–57.25 mm were selected to determine differences in geometrical soil pore parameters between CC and NCC. These two depths were separately analyzed with a personal computer, and the procedure took approximately 15 h for each soil depth in each core.An image stack consisting of 692 images was selected using the “Import image sequence” function and was segmented using the “threshold” function (Sauvola & Pietikäinen, 2000). A threshold value of 80 was used to separate pores from soil. This threshold level was selected after thresholding 60 images at different depths and taking the average to represent a threshold value for the whole soil core. These threshold values ranged from 71 to 92. Segmented 3D image stack was analyzed for macro‐ and mesoporosity and individual pore volumes by the tool 3D objects counter (Lee et al., 2008). Effective pore radii >50 μm and <50 μm were considered as macro‐ and mesopores, respectively (in relation to Table 2, pore size classification, in the SSSA's Glossary of Soil Science Termsglossary website (https://www.soils.org/publications/soils‐glossary). The Skeletonize 3D plugin and the Analyze skeleton tool were used to extract the macropore skeletons derived by the centerlines of the pores (Müller et al., 2018). This program generated pore coordination numbers for each node, branch complexity, and node density values. The mean tortuosity was calculated as the ratio between actual total macropore length and total straight‐line distance of all the macropores in the soil core (Luo et al., 2010). The plugin 3D viewer was used to develop the visual 3D representation of pore networks in soil columns after thresholding to the value 80.Statistical analysisThe SAS (version 9.4; SAS Institute, 2013) software package was used for statistical analysis of data. We used proc insight and proc univariate plot normal functions to test data for normality. No transformation of data was carried out since the data were normally distributed. An ANOVA procedure was used to identify the differences in pore characteristics between treatments. The LSD test was used for comparing the treatment means at the P < .05 level.RESULTS AND DISCUSSIONVisualization of pore networksA 3D visualization of macropores in soil cores showed distinct differences between CC and NCC treatments (Figure 1). The magenta‐colored areas in Figure 1 inside the circular rings represent pores in soil, and the colored structure density is greater in CC than in NCC. This difference was observed in all replicate soil cores, especially in the 7.25‐to‐27.25‐mm depth class. The visual observations demonstrated reducing pore network densities with increasing soil depth in both CC and NCC. Image slices segmented to the threshold value of 80 at different depths also demonstrated the same pattern in two‐dimensional binary plains (Figure 2). A study conducted in Pennsylvania to identify differences in macropore networks between crop and pasture lands observed similar reduction of pore network densities with increasing soil depth and visual differences between treatments (Luo et al., 2010).1FIGURETop view of identical three‐dimensional (3D) images at 17.25‐to‐27.25‐mm depth in cover crop (CC) and no cover crop (NCC) treatments obtained from 3D Viewer tool and segmentation in ImageJ at threshold level of 80. Magenta color in 3D images represents binary values between 0 and 80: 0 for air, and 81 being the lower threshold for soil2FIGURETwo‐dimensional projections by soil depths in cover crop (CC) and no cover crop (NCC) treatments after segmentation at threshold level of 80 by ImageJ. Red color represents binary values between 0 and 80: 0 for air, and 81 being the lower threshold for soilPore volume distributionThe pore size distribution in tested soil core samples ranged between 0.0001 and 4.4333 mm3. The largest recorded pore volume of 4.4333 mm3, observed at 7.25‐to‐27.25‐mm depth in the CC treatment, was significantly greater than that in NCC (Table 2).However, the largest pore volume at 37.25‐to‐57.25‐mm depth was not significantly different between CC and NCC treatments. The total pore volume at 7.25‐to‐27.25‐mm depth (52.11 mm3, Figure 3a) of CC was nine times greater than that as 37.25‐to‐57.25‐mm depth (5.74 mm3, Figure 3b). Similarly, even under the NCC treatment, the total pore volume was significantly greater at 7.25‐to‐27.25‐mm depth than at 37.25‐to‐57.25‐mm depth. At both considered soil depth classes, the total pore volume was significantly greater in CC than in NCC, with eight and two times greater values in CC at depths of 7.25–27.25 mm and 37.25–57.25 mm, respectively.3FIGUREThe total pore volume and pore volume by effective pore radius (μm) for cover crop (CC) and no cover crop (NCC) treatments for (a) 7.25‐to‐27.25‐mm and (b) 37.25‐to‐57.25‐mm depths. Different lowercase letters show significant differences between treatments at P < .05 level. Note that the y axis scale is different for two graphsAssuming all observed pores were circular, the effective pore radius (EPR) was calculated, and the resulted EPR ranged from 19 to 305 μm. Based on the EPR, the pore volume was classified into three pore classes: the first group with EPR < 50 μm, second with EPR = 50–100 μm, and the third with EPR > 100 μm. At 7.25‐to‐27.25‐mm depth, the first pore class had significantly greater proportion of total pore volume than the other two classes in CC (Figure 3a). In NCC, there were no pores in the third pore class, and there were no differences in pore volume proportions between first and second classes (Figure 3a). The pore volume in the first class was significantly greater in CC than in NCC at 7.25‐to‐27.25‐mm depth (Figure 3a). Interestingly, at 37.25‐to‐57.25‐mm depth, there were no pores observed in the third pore class in both CC and NCC treatments (Figure 3b). The pore volume in the first pore class was significantly greater in both CC and NCC treatments than that in the second pore class. However, the difference between two treatments was not significant in the first pore class but was significant in the second pore class (Figure 3b). The results indicate that the total pore volume was significantly greater in CC than in NCC at both soil depths, and the larger pores are concentrated in the top soil depths in both CC and NCC. Similar to our findings, Luo et al. (2010) reported decreasing pore sizes with increasing soil depth, and Müller et al. (2018) reported smaller pores in subsoil of Andisol compared with topsoil.Pore count and porosity distributionThe 3D visualization of total pores in CC and NCC treatments at top soil depth is shown in Figure 4, where the CC treatment shows a greater pore distribution compared to NCC. The green color pore network is denser in CC throughout the volume, whereas the density in NCC reduces with increasing soil depth (Figure 4). Observed pores had two distinct characteristics where some had branches (defined in Table 1) and some were individual. Thus, the total pore count was categorized into two major groups as individual pores and branched pores. The totals for branched and individual pore counts were significantly greater in CC compared with NCC at both soil depths (Figure 5). The branched pore count was not significantly different between the two treatments at both soil depths; however, the individual pore count was significantly greater compared to branched pores both in CC and NCC treatments.4FIGUREComputed tomography measured three‐dimensional (3D) visualization of pore geometry as affected by cover crop (CC) and no cover crop (NCC) treatments for the top soil depth. Soil pores are shown in green color1TABLETerminology used in the manuscriptTermDefinitionPorosity (%)Ratio between pore volume and evaluated soil core volumeMean tortuosityRatio between total actual pore length and total straight‐line distanceA skeletonA pore consisting of more than one branch and one or more junctionsLongest path lengthThe longest center line of a pore networkBranchesPore lengths attached to the longest path at nodesNodesA point where two or more branches meetIndividual poresA pore without nodes or branches attachedBranched poresA pore with at least one branch attachedTriple pointsNodes with exactly three branchesQuadruple pointsNodes with exactly four branchesCoordination numberNumber of branches meeting at one node5FIGUREDistribution of total, individual, and branched pore count for cover crop (CC) and no cover crop (NCC) treatments for 7.25‐to‐27.25‐mm and 37.25‐to‐57.25‐mm depths. Different lowercase letters show significant differences between treatments at the P < .05 levelThe porosity distribution in each 450‐mm2 by 1‐mm soil volume showed significant differences between CC and NCC treatments from 7.25‐ to 19.25‐mm depths (Figure 6). Also, the porosity decreased significantly with increasing soil depth and the rate of decrease was greater in CC compared with NCC. The mean porosity percentage of CC soil for 7.25‐to‐27.25‐mm soil depth volume was 10 times greater than that of NCC (Table 2). However, the difference was not significant at 37.25‐to‐57.25‐mm depth (Table 2). In our study, the porosity values at 7.25‐to‐27.25‐mm depth ranged from 0.01 to 1.2% for CC and from 0 to 0.06% for NCC. Singh et al. (2021) reported 0.079 mm3 mm−3 of porosity under dairy manure application in a study conducted in South Dakota (USA), whereas Lindquist et al. (2000) reported porosity values ranging from 7.5‐22% for a Fontainebleau sandstone. Udawatta, Gantzer, et al. (2008) reported greater porosity under grass and agroforestry buffer treatments compared to row crop management. A study conducted in apple orchards in New Zealand reported 7.5% macroporosity under organic orchard management system (Deurer et al., 2009). The CC root growth and the movement of other macro‐ and mesofauna living in the CC root habitats may have increased the porosity especially at shallower depths in CC soil compared with NCC. Greater porosity also could be attributed to increased microbial activity and biopores (Kodešová et al., 2006).6FIGUREμ‐Computed tomography measured porosity (%) by soil depth for cover crop (CC) and no cover crop (NCC) treatments for 7.25‐to‐27.25‐mm soil depth. The LSD line shows significant differences between treatments at the P < .05 level2TABLEPore network characteristics for 7.25‐to‐27.25‐mm and 37.25‐to‐57.25‐mm depths for cover crop (CC) and no cover crop (NCC) treatments imaged at 29‐μm resolution7.25–27.25 mm37.25–57.25 mmCharacteristicCCNCCCCNCCLargest pore volume, mm34.4333a0.1147b0.5423b0.1270bPorosity, %0.5701a0.0577b0.0737b0.0251bMean tortuosity1.9397a1.6977a1.7375a1.6977aNo. of branches151,595a46,036b108,149ab3,8061cTotal branch length, mm76,387a17,883b41,620b14,593bMean branch length, mm2.29a2.35a2.22a2.17aLongest branch length, mm15.26a14.03ab15.35a12.60bLongest path length, mm99.73a98.28a119.27a95.31aNo. of nodes58,438a20,477b43,788ab16,569bNo. of triple points28,908a10,282b21,249ab8,310bNo. of quadruple points12,866a4,937b10,192ab4,019bNote. Values followed by different letters within a parameter denote significant differences between treatments at each depth at the P < .05 level determined by least significant difference mean separation.Pore network characteristicsA pore skeleton can have many branches, and those evaluated soil volumes contained many skeletons (Table 1). The total number of branches were significantly greater in CC compared with NCC for both 7.25‐to‐27.25‐mm and 37.25‐to‐57.25‐mm soil depths with 3.3 and 2.8 times greater values in CC at two depths, respectively (Table 2). The total branch length was significantly greater in CC compared with NCC at 7.25‐to‐27.25‐mm depths, but the difference was not significant at 37.25‐to‐57.25‐mm depths. The longest branch length at the 37.25‐to‐57.25‐mm depth was significantly greater in CC than in NCC, with a mean value of 15.26 mm. The mean branch length was not significantly different between treatments or by soil depths. Udawatta, Gantzer, et al. (2008) reported branch lengths for row crop management ranging from 0.07 to 16 mm. The length of the longest path was also similar between both CC and NCC treatments across soil depths. However, the total number of nodes, number of triple points, and number of quadruple points were significantly greater in CC compared with NCC at the 7.25‐to‐27.25‐mm depth and were not significantly different between treatments at the 37.25‐to‐57.25‐mm depth. These results indicate that the CC soil has a greater complexity of pore networks and a greater connectivity with higher branch numbers and nodes compared with NCC. This can improve soil aeration and provide more space for the growth of soil flora and fauna.Coordination number and tortuosityNumber of branches meeting at one node is known as the coordination number (Udawatta et al., 2016), and our results found that the number of pore bodies with coordination numbers from 1 to 4 at 7.25‐to‐27.25‐mm depth and coordination numbers from 1 to 6 at 37.25‐to‐57.25‐mm depth were significantly greater in CC compared with NCC (Figure 7), suggesting a greater complexity of pore networks under CC. Theoretically, the coordination number should increase with increasing pore size; however, the coordination number is determined by the voxel resolution of the image analysis (Seright et al., 2001). In contrast, Al‐Raoush (2002) reported smaller coordination numbers for larger diameter particles and larger for smaller diameter particles.7FIGUREDistribution of coordination numbers between cover crop (CC) and no cover crop (NCC) treatments for 7.25‐to‐27.25‐mm and 37.25‐to‐57.25‐mm depths. Error bars show significant differences between treatments at the P < .05 levelThe tortuosity calculation of our study found no significant differences between CC and NCC treatments and among soil depths (Figure 8). Tortuosity values ranged from 1.74 to 2.23 for CC at 7.25‐to‐27.25‐mm depth, 1.62 to 1.88 for CC at 37.25‐to‐57.25‐mm depth, 1.60 to 1.90 for NCC at 7.25‐to‐27.25‐mm depth, and 1.63 to 1.75 for NCC at 37.25‐to‐57.25‐mm depth. This could be due to greater straight‐line distance between end points of pores with greater actual lengths and shorter straight‐line distance between end points of pores with shorter actual lengths. Similarly, Lou et al. (2010) reported tortuosity values of 1.53 for crop and 1.75 for pasture management.8FIGUREDistribution of mean tortuosity between cover crop (CC) and no cover crop (NCC) treatments for 7.25‐to‐27.25‐mm and 37.25‐to‐57.25‐mm depths. Different lowercase letters show significant differences between treatments at the P < .05 levelComputed tomography analyzed geometrical pore distribution features and their characteristics link to soil functions such as gas exchange, solute transport, and water flow (Deurer et al., 2009; Katuwal et al., 2015; Paradelo et al., 2016; Rabbi et al., 2016). Identification of such linkages could be used to develop better management plans and thereby improve soil quality. Overall, our study results indicate that the CC soil has greater soil porosity and a complex pore network compared with NCC, which could improve the microbial habitats and benefit the growth of other soil organisms and enhanced soil water dynamics.CONCLUSIONSOur study results showed that CC has increased μ‐tomography measured soil porosity, total pore volume, pore count, and the pore network complexity in terms of branch number, coordination number, and number of nodes compared with NCC. However, the intensity of above parameters reduces with increasing soil depth despite the CC or NCC treatment. The study highlights the increased benefits of CC in micrometer scale resolution in terms of soil pore network characteristics.ACKNOWLEDGMENTSWe gratefully acknowledge the funding from The Mini Grant Program of the School of Natural Resources, University of Missouri, and USDA‐NRCS Missouri State Office for the study. We also thank the University of Missouri Bradford Research Center and the staff for the study site and their support. The research was also supported by The Center for Agroforestry, University of Missouri, Columbia, and the USDA‐ARS Dale Bumpers Small Farm Research Center, Agreement Number 58‐6020‐0‐007 from the USDA Agricultural Research Service.AUTHOR CONTRIBUTIONSLalith Mahendra Rankoth: Data curation; Formal analysis; Methodology; Software; Visualization; Writing – original draft; Writing – review & editing. Ranjith P. Udawatta: Conceptualization; Funding acquisition; Project administration; Resources; Supervision. Clark J. Gantzer: Conceptualization; Supervision; Validation. Stephen H. Anderson: Resources; Supervision; Validation.CONFLICT OF INTERESTThe authors declare no conflict of interest.REFERENCESAl‐Raoush, R. I. (2002). Extraction of physically‐realistic pore network properties from three‐dimensional synchrotron microtomography images of unconsolidated porous media [Doctoral dissertation, Louisiana State University]. https://digitalcommons.lsu.edu/cgi/viewcontent.cgi?article=2165&context=gradschool_dissertationsArachchige, P. S. P., Hettiarachchi, G. M., Rice, C. W., Dynes, J. J., Maurmann, L., Wang, J., Karunakaran, C., Kilcoyne, A. L. D., Attanayake, C. P., Amado, T. J. C., & Fiorin, J. E. (2018). Sub‐micron level investigation reveals the inaccessibility of stabilized carbon in soil microaggregates. Science Reports, 8, 16810. https://doi.org/10.1038/s41598‐018‐34981‐9Blunt, M. J., Bijeljic, B., Dong, H. U., Gharbi, O., Iglauer, S., Mostaghimi, P., Paluszny, A., & Pentland, C. (2013). Pore‐scale imaging and modelling. Advances in Water Resources, 51, 197–216. https://doi.org/10.1016/j.advwatres.2012.03.003Carof, M., De Tourdonnet, S., Coquet, Y., Hallaire, V., & Roger‐Estrade, J. (2007). Hydraulic conductivity and porosity under conventional and no‐tillage and the effect of three species of cover crop in northern France. Soil Use and Management, 23, 230–237. https://doi.org/10.1111/j.1475‐2743.2007.00085.xCercioglu, M., Anderson, S. H., Udawatta, R. P., & Haruna, S. I. (2018). Effects of cover crop and biofuel crop management on computed tomography‐measured pore parameters. Geoderma, 319, 80–88. https://doi.org/10.1016/j.geoderma.2018.01.005Chen, G., & Weil, R. R. (2010). Penetration of cover crop roots through compacted soils. Plant and Soil, 331, 31–43. https://doi.org/10.1007/s11104‐009‐0223‐7Daniel, J. B., Abaye, A. O., Alley, M. M., Adcock, C. W., & Maitland, J. C. (1999). Winter annual cover crops in a Virginia no‐till cotton production system: II. Cover crop and tillage effects on soil moisture, cotton yield, and cotton quality. Journal of Cotton Science, 3, 84–91.Deurer, M., Grinev, D., Young, I., Clothier, B. E., & Müller, K. (2009). The impact of soil carbon management on soil macropore structure: A comparison of two apple orchard systems in New Zealand. European Journal of Soil Science, 60, 945–955. https://doi.org/10.1111/j.1365‐2389.2009.01164.xDing, G., Liu, X., Herbert, S., Novak, J., Amarasiriwardena, D., & Xing, B. (2006). Effect of cover crop management on soil organic matter. Geoderma, 130, 229–239. https://doi.org/10.1016/j.geoderma.2005.01.019Doran, J. W., & Zeiss, M. R. (2000). Soil health and sustainability: Managing the biotic component of soil quality. Applied Soil Ecology, 15, 3–11. https://doi.org/10.1016/S0929‐1393(00)00067‐6Fageria, N. K., Baligar, V. C., & Bailey, B. A. (2005). Role of cover crops in improving soil and row crop productivity. Communications in Soil Science and Plant Analysis, 36, 2733–2757. https://doi.org/10.1080/00103620500303939Gantzer, C. J., & Anderson, S. H. (2002). Computed tomographic measurement of macroporosity in chisel‐disk and no‐tillage seedbeds. Soil and Tillage Research, 64, 101–111. https://doi.org/10.1016/S0167‐1987(01)00248‐3Johnson, J. M. F., Strock, J. S., Tallaksen, J. E., & Reese, M. (2016). Corn stover harvest changes soil hydrology and soil aggregation. Soil and Tillage Research, 161, 106–115. https://doi.org/10.1016/j.still.2016.04.004Katuwal, S., Moldrup, P., Lamandé, M., Tuller, M., & De Jonge, L. W. (2015). Effects of CT number derived matrix density on preferential flow and transport in a microporous agricultural soil. Vadose Zone Journal, 1(7), https://doi.org/10.2136/vzj2015.01.0002Kodešová, R., Kodeš, V., Žigová, A., & Šimůnek, J. (2006). Impact of plant roots and soil organisms on soil micromorphology and hydraulic properties. Biologia, 61, S339–S343. https://doi.org/10.2478/s11756‐006‐0185‐7Kozlowski, T. T. (1999). Soil compaction and growth of woody plants. Scandinavian Journal of Forest Research, 14, 596–619. https://doi.org/10.1080/02827589908540825Lal, R. (2015). Restoring soil quality to mitigate soil degradation. Sustainability, 7, 5875–5895. https://doi.org/10.3390/su7055875Lee, S. S., Gantzer, C. J., Thompson, A. L., Anderson, S. H., & Ketcham, R. A. (2008). Using high‐resolution computed tomography analysis to characterize soil‐surface seals. Soil Science Society of America Journal, 72, 1478–1485. https://doi.org/10.2136/sssaj2007.0421Lindquist, W. B., Venkatarangan, A., Dunsmuir, J., & Wong, T.‐F. (2000). Pore and throat size distributions measured from synchrotron x‐ray tomographic images of Fontainebleau sandstones. Journal of Geophysical Research, 105B, 21509–21528. https://doi.org/10.1029/2000JB900208Lipiec, J., & Hatano, R. (2003). Quantification of compaction effects on soil physical properties and crop growth. Geoderma, 116, 107–136. https://doi.org/10.1016/S0016‐7061(03)00097‐1Luo, L., Lin, H., & Li, S. (2010). Quantification of 3‐D soil macropore networks in different soil types and land uses using computed tomography. Journal of Hydrology, 393, 53–64. https://doi.org/10.1016/j.jhydrol.2010.03.031Montgomery, D. R. (2007). Soil erosion and agricultural sustainability. Proceedings of the National Academy of Sciences, 104, 13268–13272. https://doi.org/10.1073/pnas.0611508104Müller, K., Katuwal, S., Young, I., Mcleod, M., Moldrup, P., De Jonge, L. W., & Clothier, B. (2018). Characterising and linking X‐ray CT derived macroporosity parameters to infiltration in soils with contrasting structures. Geoderma, 313, 82–91. https://doi.org/10.1016/j.geoderma.2017.10.020Nickerson, C., Ebel, R., Borchers, A., & Carriazo, F. (2011). Major uses of land in the United States, 2007 (Economic Information Bulletin 89). USDA.Obalum, S. E., Igwe, C. A., Obi, M. E., & Wakatsuki, T. (2011). Water use and grain yield response of rainfed soybean to tillage‐mulch practices in southeastern Nigeria. Scientia Agricola, 68, 554–561. https://doi.org/10.1590/S0103‐90162011000500007Paradelo, M., Katuwal, S., Moldrup, P., Norgaard, T., Herath, L., & de Jonge, L. W. (2016). Xray CT‐derived soil characteristics explain varying air, water, and solute transport properties across a loamy field. Vadose Zone Journal, 15(4), https://doi.org/10.2136/vzj2015.07.0104Peth, S., Chenu, C., Leblond, N., Mordhorst, A., Garnier, P., Nunan, N., Pot, V., Ogurreck, M., & Beckmann, F. (2014). Localization of soil organic matter in soil aggregates using synchrotron‐based X‐ray microtomography. Soil Biology & Biochemistry, 78, 189–194. https://doi.org/10.1016/j.soilbio.2014.07.024Rabbi, S. M. F., Daniel, H., Lockwood, P. V., Macdonald, C., Pereg, L., Tighe, M., Wilson, B. R., & Young, I. M. (2016). Physical soil architectural traits are functionally linked to carbon decomposition and bacterial diversity. Science Reports, 6, 33012. https://doi.org/10.1038/srep33012Rachman, A., Anderson, S. H., & Gantzer, C. J. (2005). Computed‐tomographic measurement of soil macroporosity parameters as affected by stiff‐stemmed grass hedges. Soil Science Society of America Journal, 69, 1609–1616l. https://doi.org/10.2136/sssaj2004.0312Rasiah, V., & Alymore, L. A. G. (1998). The topology of pore structure in cracking clay soil: I. The estimation of numerical density. European Journal of Soil Science, 39, 303–314. https://doi.org/10.1111/j.1365‐2389.1988.tb01217.xSAS Institute. (2013). SAS user's guide: Statistics. SAS Institute.Sauvola, J., & Pietikäinen, M. (2000). Adaptive document image binarization. Pattern Recognition, 33, 225–236. https://doi.org/10.1016/S0031‐3203(99)00055‐2Schindelin, J., Arganda‐Carreras, I., Frise, E., Kaynig, V., Longair, M., Pietzsch, T., Preibisch, S., Rueden, C., Saalfeld, S., Schmid, B., Tinevez, J.‐Y., White, D. J., Hartenstein, V., Eliceiri, K., Tomancak, P., & Cardona, A. (2012). Fiji: An open‐source platform for biological‐image analysis. Nature Method, 9, 676. https://doi.org/10.1038/nmeth.2019Seright, R. S., Liang, J., Lindquist, W. B., & Dunsmuir, J. H. (2001). Characterizing disproportionate permeability reduction using synchrotron x‐ray computed tomography [Paper SPE‐71508MS]. SPE Annual Technical Conference and Exhibition, New Orleans, LA. https://doi.org/10.2118/71508‐MSSingh, N., Kumar, S., Udawatta, R. P., Anderson, S. H., De Jonge, L. W., & Katuwal, S. (2021). X‐ray micro‐computed tomography characterized soil pore network as influenced by long‐term application of manure and fertilizer. Geoderma, 385, 114872. https://doi.org/10.1016/j.geoderma.2020.114872Smart, J. R., & Bradford, J. M. (1996). No‐tillage and reduced tillage cotton production in south Texas. In P. Dugger & D. A. Richter (Eds.), Proceedings of the Beltwide Cotton Conference (pp. 1397–1401). National Cotton Council of America.Stirzaker, R., & White, I. (1995). Amelioration of soil compaction by a cover‐crop for no‐tillage lettuce production. Australian Journal of Agricultural Research, 46, 553–568. https://doi.org/10.1071/AR9950553Udawatta, R. P., Anderson, S. H., Gantzer, C. J., & Garrett, H. E. (2006). Agroforestry and grass buffer influence on macropore characteristics: A computed tomography analysis. Soil Science Society of America Journal, 70, 1763–1773. https://doi.org/10.2136/sssaj2006.0307Udawatta, R. P., Anderson, S. H., Gantzer, C. J., & Garrett, H. E. (2008). Influence of prairie restoration on CT‐measured soil pore characteristics. Journal of Environmental Quality, 37, 219–228. https://doi.org/10.2134/jeq2007.0227Udawatta, R. P., Gantzer, C. J., Anderson, S. H., & Garrett, H. E. (2008). Agroforestry and grass buffer effects on pore characteristics measured by high‐resolution X‐ray computed tomography. Soil Science Society of America Journal, 72, 295–304. https://doi.org/10.2136/sssaj2007.0057Udawatta, R. P., Gantzer, C. J., Reinbott, T. M., Wright, R. L., & Pierce, R. A. (2016). Yield differences influenced by distance from riparian buffers and conservation reserve program. Agronomy Journal, 108, 647–655. https://doi.org/10.2134/agronj2015.0273Van Noordwijk, M., Widianto, Heinen, M., & Hairiah, K. (1991). Old tree root channels in acid soils in the humid tropics: Important for crop root penetration, water infiltration, and nitrogen management. Plant and Soil, 134, 37–44. https://doi.org/10.1007/BF00010715Williams, S. M., & Weil, R. R. (2004). Crop cover root channels may alleviate soil compaction effects on soybean crop. Soil Science Society of America Journal, 68, 1403–1409. https://doi.org/10.2136/sssaj2004.1403Young, I. M., & Ritz, K. (2000). Tillage, habitat space and function of soil microbes. Soil and Tillage Research, 53, 201–213. https://doi.org/10.1016/S0167‐1987(99)00106‐3
"Agrosystems, Geosciences & Environment" – Wiley
Published: Jan 1, 2022
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