Spatial variability of pores in oxidic latosol under a conservation management system with different gypsium doses.

A estrutura do solo e alterada quando submetida ao processo agricola, ou seja, uma nova organizacao espacial do sistema poroso e formada com reflexo na qualidade fisica do mesmo. Dessa forma, objetivou-se, neste trabalho, visualizar e quantificar, por meio da tomografia computada de raios-X, a variabilidade na distribuicao do diâmetro de poros em Latossolo oxidico submetido a um sistema de manejo conservacionista que utiliza diferentes doses de gesso. Foram abertas tres trincheiras aleatorias e longitudinais a linha de plantio em um Latossolo Vermelho distrofico gibbsitico muito argiloso, submetido as seguintes doses de gesso: G0: ausencia de gesso; G7: 7 Mg ha-1 e G28: 28 Mg ha-1 de gesso adicional, aplicados na superficie do solo na linha de plantio. Amostras com estrutura preservada foram coletadas em cilindros de acrilico, nas profundidades de 0,20-0,34, 0,80-0,94 e 1,50-1,64 m, apos seis anos de cultivo dos cafeeiros ,para quantificacao dos poros 3D, detectados pela tomografia computada de raios-X. A variabilidade espacial da estrutura do solo foi avaliada por meio de semivariogramas gerados a partir das imagens 3D na escala de cinza. A distribuicao do diâmetro dos poros detectaveis foi feita por meio da mineracao de dados. Paras as inferencias estatisticas, foram utilizados os pacotes 'geoR' para os semivariogramas e 'randomForest' para a mineracao de dados em linguagem R. A maior continuidade espacial dos poros ocorreu no tratamento G7 nas tres profundidades. Os efeitos combinados do sistema de manejo promoveram a maior variabilidade espacial da estrutura do solo no tratamento G28.Com base nos semivariogramas, pode-se inferir que a adocao do sistema em estudo promoveu modificacoes na rede de poros em todas as direcoes (X, Y e Z), porem com melhor continuidade dos poros na direcao vertical (Z).


INTRODUCTION
The management of agricultural systems affects the soil physical attributes and thus the pore spatial organization, which justifies studies that seek to evaluate soil structural quality. Soil structure is formed by aggregates and a wide network of pores, especially inter-aggregate pores, ensuring good functioning of the hydric, physico-chemical and biological processes and plant performance (Luo;Lin, Li, 2010;Martin et al. 2012).
In these Latosols, when subjected to agricultural management systems, the inter-aggregate pores are seriously affected by the first few passes of machinery, given its high susceptibility to compaction (Severiano et al. 2013.) Therefore, any alteration made by anthropic factors should be evaluated, among other purposes, to detail the possible causes of spatial pore variability (Munkholm;Heck;Deen, 2012;Luo;Lin;Li, 2010).
In this context, the use of a management system that employs conservation practices in coffee crops in the Cerrado region is gaining popularity for combining chemical and physical improvements in soil -water relationships acheive to rising productivity and pursuing of environmental sustainability. Within the principles of conservation agriculture (Raij, 2008), this system provides conditions that enhance the coffee root system performance, so that there are improvements in the utilization of the available water at depth and efficient use of nutrients distributed in the soil profile (Serafim et al. 2011).
For the evaluation of changes in soil porosity promoted by management, X-ray CT scan is effective for enabling three-dimensional mapping of the soil structural components, allowing a qualitative and quantitative evaluation (Borges;Pires, 2012).
So, the aim of this work was to visualize and quantify, through X-ray CT scan, the pore distribution in a oxidic Latosol submitted to a conservation management system with different gypsum doses.

Study area
The study was conducted in a coffee plantation in the municipality of São Roque de Minas, physiographic region of the Upper São Francisco, MG, whose coordinates are 20º15'45" S e 46º18'17" W with 850m altitude. The experimental cultivation was established in November/2008 and it has been conducted according to the practices of a soil conservation management system. The region climate is Cwa, according to the Köppen classification, with annual precipitation of 1,344 mm and a well-defined dry season from May to September (Menegasse;Gonçalves;Fantinel, 2002).
According to the premises of the conservation management system of coffee (Coffea arabica L.) crop, the cultivar yellow Catucaí was planted in a narrow row spacing of 2.50x0.65m (between plants and between rows, respectively). Tillage was conducted using one plowing and two harrowings with application of amendments throughout the total area (4 Mg ha -1 dolomitic limestone + 1.92 Mg ha -1 agricultural gypsum).
We used a subsoiler followed by a spade applied fertilizer for the opening of the 0.60 m deep and 0.50 m wide furrow and mixed the soil at 0.40 m depth (Ticianel, 2013), allowing the incorporation of basic fertilizer (formula 08-44-00, enriched with 1.5% Zn and 0.5% B). This furrow was corrected at greater depth with dolomitic limestone 8 Mg ha -1 (2 kg m -1 ) in all treatments. The coffee seedlings were planted between the second half of October and the first half of November 2008.
After planting, the treatments received different doses of additional gypsum, this amendment being covered with soil material and mixed with the inter-row plant material (mixture piled-up at the base of the coffee plant stem).
Together with the installation of the crop, Brachiaria ducumbens (Syn. Urochloa) was implanted between the planting lines, which were periodically cut with a brush cutter, which minimizes competition with the main crop and allows the plant residue produced to be distributed along the row as well as between rows. This practice is used to improve soil structure and serves as protection against erosive agents (Lima et al. 2012). The related crop operations were carried out by means of animal traction equipment; only the harvesting was done mechanically. The nutritional monitoring and fertilization management of the coffee crop was conducted based on leaf analysis (Serafim et al. 2011;Carducci et al. 2013).
The parcels contain 10 rows with 36 plants each, totaling 360 plants per plot with an area of 585 m 2 . The boundary corresponds to 3 plants at the beginning of the plot and two rows on the sides, totaling 360 m 2 .
For this study treatments were included: G0absence of additional gypsum; G7 -7 Mg ha -1 and G28 -28Mg ha -1 of additional gypsum, both applied on the surface of the plant row. The selection of these treatments was based on the hypothesis of possible structural alterations promoted by the application of gypsum and for this we evaluated the recommended dose ages found in the literature (G7), the reference dose for the system adopted itself (G28) and the treatment without additional gypsum (G0).

Soil sampling and physical, chemical and mineralogical characterization
For the sampling and characterization of soil, three random trenches were dug lengthwise along the plant row, with dimensions of 0.70m (width) x 1.50 m (length) x 1.50 m (depth) and then samples were collected for physical, chemical and CT scan analyses. It is noteworthy to mention that at the sampling time (September/2011) the crop had three years of cultivation.
The intact soil cores were sampled by hand, using plexiglass cylinders (0.065 m diameter and 0.14 m height) equipped with a specially designed aluminum sampling ring for CT scan analysis and more volumetric rings (0.065 cm diameter and 0.025 m height) for the remaining analyzes. The sampling occurred between plants (0.65 m) and just below the gypsum layer at depths of 0.20-0.34, 0.80-0.94 and 1.50-1.64 m, with three replicates for each treatment (G0, G7 and G28), totaling 27 samples.
In the laboratory, the disturbed soil samples collected rightly after the undisturbed samples collection were air-dried and passed through a two-millimeter mesh sieve for further analyze.
The SiO 2 , Al 2 O 3 and Fe 2 O 3 contentswere determined by sulfuric acid digestion and used in calculations of the Ki (SiO 2 /Al 2 O 3 ) and Kr [SiO 2 / (Al 2 O 3 + Fe 2 O 3 )] molecular ratios (Embrapa, 2013). The values of Ki and Kr less than 0.75 characterize this soil as very weathered, which is evidence of its sesquioxidic gibbsitic mineralogy. The kaolinite and gibbsite contents were derived from XRD measurements and stoichiometric ratios derived from their ideal chemical formulas, according to Resende, Bahia Filho and Braga(1987) (Table 1).
The particle size analysis was performed by employing slow agitation of the soil suspension, using NaOH 1mol L -1 for 16 hours. Mean values in g kg -1 were, respectively for clay, silt and sand: 819, 157 and 24 at a depth of 0.20-0.34 m; 848, 127 and 25 at a depth of 0.80-0.94 m and 886, 89 and 25 at a depth of 1.50-1.64 m. Subsequently, we conducted fertility analyzes, where aliquots of these samples were taken to determine the pH, sorptive complex and organic matter contents of the soil (Embrapa, 2011) ( Table 2).

Acquisition, reconstruction and binarization (Thresholding) of the 3D images
For the CT scan the soil cores in plexiglass cylinders were dehydrated in an oven at 40° C until a constant weight, aiming to minimize possible interference by water films on the X-rays attenuation, and then scanned at 120kV and 170 mA with an integration time of 3500 mS, generating an 2D axial projection of X-ray attenuation imagery, in microCT scan (EVS/GE MS8X-130),thirdgeneration preclinical, cone-beam, equipped with a tungsten X-ray tube. Excitation energy of 100 kV and 130 mA was employed for all samples, in the Soil Image Laboratory at University of Guelph, Canada.
In order to obtain greater accuracy in the analysis and elimination of possible structural alterations generated during the sampling, a 0.033 m slice from the middle of the core was selected for scanning. As the X-ray source emits polychromatic X-rays (Clausnitzer;Hopmans, 2000), we employed prefiltering with a high-pass copper foil (0.5 mm) in order to reduce beam-hardening artifacts and to maximize the contrast between the different phases of the soil core (solid and air).
The 2D axial projections were acquired and reconstructed with 20µm spatial resolution (pixel size) and they were saved in 16-bit radiometric resolution. Then 3D subvolumes of interest were selected in the exact center of each original image and the reconstruction was done using a proprietary filtered back-projection software called ''eXplore Reconstruction Utility'' ( GE Healthcare, 2006). The final isometric volume (666 voxel x 666 voxel x 550 slice) was reconstructed at 60 µm voxel size, aiming to maximize both region of interest and spatial resolution, within a manageable 500 MB file size ( Figure 1).   Ciênc. Agrotec., Lavras, v.38, n.5, p.445-460, set./out., 2014 For the purpose of comparing the X-rays attenuation, in CT imagery, we used the values in Hounsfield scale (defined relative to air [-1000 HU] and water [0 HU]) and a calibration procedure through the use of two capillary tubes (one filled with water and the other with air inserted between the inner wall of the plexiglass tube and the sample at the time of scanning). Subsequently, the coefficients of water and air were calculated and a Gaussian smoothing filter (radius of 1 voxel) was used to reduce image noise and artifacts with the aid of MicroView ( GE Healthcare, 2006) prior to subsequent analyses in NIH ImageJ (Rasband,2012).

3D image processing and analyzes
The 3D image processing followed the protocol of the Soil Image Laboratory, University of Guelph, Canada. This is the first step in 3D image processing, which involves the conversion of each grayscale voxel value of the grayscale image (proportionally expressing the locations of the X-rays attenuation coefficients) into a binary image, distinguishing the void and non-void in the selected subvolume images.
The thresholding was done in NHI ImageJ (Rasband, 2012), by a method based on the work of Schlüter, Weller and Vogel (2010) employing both Laplacian edge detection and seeded-region-growing to assign the voxels associated with the zero-crossing, but first enforcing clamping of the gray-scale image (considering the histogram peak positions for air and solid), to minimize unnecessary edges.
With the public domain image analysis software NIH ImageJ, all analyzes were done in full 3D mode, which allowed differentiation of data categories according to the desired micro morphological size and shape classes.
To obtain the spatial data the "Semivariance 3D" plugin of the NIH ImageJ was used, with the X-rays attenuation value of the grayscale image. We obtained semivariances for the 3D image in grayscale, i.e., the orthogonal directions (X, Y, Z), where the semivariance is standardized [0-1]. The construction of the experimental semivariogram identified the spatial dependence amplitude of the variable under study and also defined the spatial variability structure (Goovaerts et al. 1999).
The "Analyze Particle" function of NHI ImageJ was used to calculate the pore dimensions (volume and area). This function detects and measures the objects in the binary image to obtain data relative to pores present in the image volume under study (n = 550). From this result the equivalent diameter to a sphere (3D images, spatial geometry simulation) and as the shape factor, the sphericity of the pore (0 = less rounded, angular, 1 = more rounded and smooth) were calculated by equation 1: where: π ≈ 3.141592…8, V = volume (mm³) and A = area (mm²).

Statistical analyses
Spatial analyses consisted of the construction of the experimental semivariogram from 3D images in grayscale.
The semivariance is a function of the distance h (Equation 2), which is estimated in a discrete set of lag distances expressed by a scatterplot that allows the variographic analysis of the spatial dependence amplitude of the variables studied (Faraco et al. 2008), in this case the X and Y orthogonal directions (horizontal direction) and Z (vertical direction), defining then the parameters required for the estimation of characteristics resulting from the spatial variability structure (Ávila, Mello;Silva, 2010). It is noteworthy that although we were working with 3 axis -directions (X, Y, Z), the calculation of semivariance was given by R², since we were working with the planes X, Y and Z. Thus, the standardized semivariograms were estimated by the classical method, through the estimator: in which: is the semivariance estimator, is the number of pairs of measured values , and, separated by a vector distance h, are realizations of the random variable (Journel, Huijbregts, 1978;Isaaks, Srivastava, 1989).
We then proceeded to estimate the experimental semivariance for each replication and using the average semivariance it was fitted to the exponential model (Equation 3), that presented the better fit of the data, and then the model parameters were determined: nugget effect (C 0 ), sill (C 0 + C 1 ) and theoretical and practical range (a), in R language (R Development Core Team, 2012), more specifically with the 'geoR' package (Ribeiro Junior, Diggle, 2001), both having free access and in accordance with the GPL (General Public License): (1) (3) ( ) Lavras, v.38, n.5, p.445-460, set./out., 2014 Subsequently prediction intervals (PI) were constructed for the set of replications, in order to compare the spatial variability, as with the PI, so it can be stated that 95% of the samples were predicted.
The detectable pores were classified based on data mining by 'randomForest' package (Liaw;Weiner,2012), and other functions developed in R language (R Development Core Team, 2012), where it was possible to generate various pore diameter classes and to detect the most contrasting ones. It generated 26 contrasting pore diameter classes distributed through a range of 0.2 to 1 mm in diameter, which corresponds to the limit between fine macropores and large mesopores (Bullock et al. 1985).

RESULTS AND DISCUSSION
The empirical semivariograms indicated secondorder stationarity for the variables evaluated, reflected by clear and well-defined sill (asymptote), which permitted identification of the spatial dependence range of the orthogonal directions (X, Y and Z) of the 3D images in grayscale. Thus, the spatial variability structure was defined (Goovaerts et al. 1999), as well as the parameters necessary to estimate its characteristics (Figures 2-10).
This assessment was needed to verify the spatial dependence of the structural components (mineral portion and pore space) in each level combination (3 treatments x 3 depths x 3 directions) obtained through the axes(X, Y and Z) of the 3D images. This allowed the identification of alterations promoted by the management system in question.
It was not possible to verify a distinct anisotropic structure only by visual observation of the semivariogram, thus by means of the prediction intervals (PI), the distinctions between events were revealed, where the 0.20-0.34 m, the G0 and the G7 showed the lowest range (a) (Figures 2 and 5), unlike G28 where a more pronounced narrowing occurred in the 0.80-0.94 m depth (Figure 9).
From the models obtained, the practical range (a) of the spatial dependence for the X, Y and Z axes, the greater (a) values were found in G7, at all depths, especially in the vertical direction (Z), followed by G0 and G28, respectively, at the 0.20-0.34 and 1.50-1.64 m depth range and the horizontal directions (X and Y) at the 0.80-0.94 m depth range.
The (a) indicates the magnitude of the spatial dependency, so that the higher range (a)in G7 relative to the other treatments suggest the higher spatial continuity of the soil structure in the X, Y and Z directions (Ávila, Mello;Silva,2010). This facts probably due to amore homogeneous pore diameter distribution (Figure 11), with a possible decrease in macroporosity, which agrees with Schaffrath et al. (2008), that a possible pores homogenization may be related to a more uniform distribution of meso-aggregates C 0 = Nugget effect was null; C 0 +C 1 = still; aT = range theoretical (mm); aP = range practical (mm).

Treatments
Depth ( Table 3 -Parameters of the exponential model fit for the orthogonal directions (X, Y, Z) for the treatments, G0: absence of additional gypsum, G7: 7 Mg ha -1 and G28: 28 Mg ha -1 additional gypsum applied on the soil surface of the plant row.
(Costa Júnior et al. 2012), which was also observed by Silva et al. (2013) at a depth of 0.15 m in G7, working in the same area of this experimental research. The formation and stability of macro-and microaggregates, which influence the distribution of space voids is strongly related primarily to the oxidic mineralogy and clay texture. This is similar to what occurs with Latosols of the chapadas of the Cerrado, due to electrostatic interactions between the oxides (mainly Al and Fe), and external agents, such as those related to the adopted management system, since the biological agents have only a secondary importance (Costa Júnior et al. 2012;Vollant-Tuduri et al. 2005).
The spatial analysis performed, revealed that the effect of external agents, was identified, which is related to the gypsum amendment associated with other agricultural practices (Table 3).
In all experimental units (27 cases), the nugget effect was zero null (Table 3), due to the adequate spatial resolution (60 µm) which generated a greater level of detail of the soil structure organization, i.e., within the limits of (a)and the still, most of the variability can be explained by the spatial component. The still had very similar values with a slight decrease in G7.
The highest IP, as well as the lowest (a) values in the 0.20-0.34 m depth, occurred in the G28 (Figure 8 and Table 3) being explained by the presence of a greater volume of inter-aggregate pores, with diameters ranging from 0.3 to 0.6 mm ( Figure 11).
The considerable occurrence of these pore diameters was most likely due to the combined effects of the management practices, primarily by the tillage occurred in preparing the plant row, that tends to homogenize the aggregates through the destruction of larger aggregates (Schaffrath et al. 2008;Cremon et al. 2009;Costa Júnior et al. 2012).   Ciênc. Agrotec., Lavras, v.38, n.5, p.445-460, set./out., 2014 These new aggregates were formed possibly by the action of biological factors (Silva et al. 2013;Martin et al. 2012), via the constant supply of the organic material throughout the soil profile, by both the mowing of the interrow Brachiaria sp. and the renewal of the coffee roots, that increase the soil microorganism activity (mycorrhizal fungi, preferentially). This once associated acttore organize soil structure and diversify aggregates into different sizes (Costa Júnior et al. 2012, Martin et al. 2012Cremon et al. 2009;Salton et al. 2008) which can promote the heterogeneity found in the pore diameter.
Accordingly, the soil management systems are considered a major source of spatial variability of soil physical properties (Schaffrathet al. 2008) and thus, exceptionally at the 0.20-0.34 m depth, the whole effect of the management practices for this system are being expressed by the pore diameter distribution.
The distinction between treatments was evident throughout the range generated especially in the classes of 0.3 to 0.6 mm in diameter with a greater number and volume of pores visible, i.e., they occupied a larger part of the soil matrix in the following order: G28 > G0 > G7, especially at the 0.20-0.34 m depth.
The increase in the pore volume with a diameter less than 0.5 mm (Figure 11) especially at the 0.20-0.34 m depth, primarily for G0 and G28, may be related to the contribution of plant residue on the soil surface. That is associated to the development of specific mycorrhizal fungi, which favor the formation of a large number of smaller pores, originated from grouping of solid particles according to the growth of hyphae with a less diameter to than those of the pores, thus forming a network of these pore with fine roots of the plants, which favors the emergence of smaller aggregates (Martin et al. 2012).
On the other hand the amount of additional gypsum applied probably has secondary effects on the aggregate formation due to similarities in the pore distribution of G0 and G28 at the 0.20-0.34m depth.
It is noteworthy that the gypsum is probably solubilized in a gradual manner, since besides being applied on the surface, it is covered with a thick layer of soil (≈ 50 cm) together with an accumulation of biomass from the inter-row cut grass, and over time, accommodation of the particles occurs over it, thus creating a gypsum reservoir. Furthermore, as the region receives uneven rainfall distribution, a gradual release of the gypsum chemical components is favored (Serafim et al. 2011).
The adverse behavior of G7 in the same depth, regarding the number and volume of pores in the detectable diameter interval, deserves further studies.
It was found that in the G7, the exchangeable calcium (Ca 2+ ) is the predominant ionic species in the soil solution, with values of 1.6 and 13 mmol c dm -3 in G0 and G7, respectively, at 0.15-0.25 m depth after 16 months of crop implantation. After 2.5 years was 27 and50 mmol c dm -3 in G0 and G7, respectively, and in G28 was 48 mmol c dm -3 (Silva et al. 2013;Ramos et al. 2013).
In G7 quantities are insufficient to act as an aggregate dispersing agent (Spera et al. 2008;Cremon et al. 2009). Even it is important to point out that the highest spatial continuity of pores found in the range evaluated could be positive if there was a proportional increase in the smaller pores number responsible for the higher retention of available water to plants.
The por spatial distribution of greater diameter throughout the soil profile influences the equilibrium of soil physical-hydric processes. The knowledge of pores spatial geometry which controls the increment in number and area of pores, as well as their tortuosity, contributes to the understanding of the alterations that may occur in the soil processes dynamics, influenced by agricultural practices (Luo;Lin;Li, 2010).
With respect to the sphericity that indicates the surface roughness of the pores (0 = roughness, 1 = rounded), it increased with decreasing pore diameter, as observed by Tippkötter et al. (2009) (Figure 11). Sphericity was similar throughout the soil profile, which is attributed to the micro-granular type structure favored mainly by the high gibbsite content (Table 1) that acts in the formation of the very small micro-aggregates and more rounded micropeds (Vidal-Torrado et al. 1999;Ferreira, Fernandes;Curi 1999).
The sphericity of the pores was remarkable at the 0.20-0.34m depth and this fact may be related to both the oxidic-gibbsitic mineralogy and the effects of soil tillage in the plant row, which tends to form smaller and more rounded aggregates, via the breakdown of the larger ones, as macro-morphologically observed by Cremon et al. (2009).

CONCLUSIONS
A greater spatial continuity of pores was detected in G7 at the depths evaluated.
A highly homogeneous distribution of the visible pores volume in each class occurred in G7 especially at 0.20-0.34m depth.
The largest pore number and volume were detected in G28 in the 0.20-0.34m depth, as well as the greatest spatial variability of soil structure, promoted by the effect of combined practices of the management system.
Based on geostatistical analyses, it can be inferred that the adoption of the management system under study promoted changes in the pore network in all directions (X, Y and Z), but with better pores continuity in the vertical direction (Z).

ACKNOWLEDGMENTS
To FAPEMIG for research funding; to Consórcio Embrapa Café for the vehicle loan; CNPq for granting the scholarship; UFLA for the institutional support; to Empresa AP for technical and logistical support. To the DCS-UFLA D.Sc. student Teotônio Soares for the R language support.