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Applicability of the photogrammetry technique to determine the volume and the bulk density of small soil aggregates.

Introduction

The soil bulk density is defined as the mass of the solids that occupy a unit volume of soil (Jury and Horton 2004). This parameter is related to total soil porosity and is commonly used as a measure of soil quality, being related to the ease of root penetration into soil, water movement, or soil strength (Grossman and Reinsch 2002). Values of bulk density depend on the texture, structure, degree of compaction, and shrink-swell characteristics of soil (Hillel 1998). Under field conditions, in soils with a certain amount of clay (>15%, <2 [micro]m), the mineral particles (sand, silt and clay) tend to form structured units known as aggregates (Horn et al. 1994). According to Horn (1990), in humid climates the bulk-density values of these aggregates range between 1.45 and 1.65g [cm.sup.-3]. The formation of soil structure due to shrinkage and swelling depends not only on the distribution of the pore space in the bulk soil but also on variations in the physical and chemical properties of single aggregates. At the same time, many physical and biological soil processes depend on the internal microscale or aggregate structure as well as on the organisation of soil aggregates (Horn 1990; Blanco-Canqui et al. 2005a). For this reason, there is increasing interest in studying the properties of individual soil aggregates to understand the behaviour of the whole soil and its response to management (Munkholm et al. 2007; Blanco-Canqui and Lai 2008; Blanco-Moure et al. 2012). Most studies have characterised the [rho] on large to medium-sized soil aggregates sizes (>4mm diameter) (e.g. Benjamin and Cruse 1985; Saleh 1993; Lipiec et al. 2012; Subroy et al. 2012), and almost no information is available for smaller aggregates (<4 mm).

In agricultural soils, tillage affects soil aggregation and bulk density by breaking up root systems and large aggregates and exposing organic matter to decomposition and loss. Bulk density of soil aggregates ([rho]) has been measured in studies that evaluate the effect of different management practices on soil physical conditions (Materechera and Mkhabela 2001; Munkholm and Schjonning 2004; Blanco-Canqui et al. 2005b). However, [rho] data from long-term conservation tillage systems are scarce and variable despite increasing interest in these alternative systems in most countries around the world (Friedrich et al. 2012).

The most common procedure used to measure [rho] is the clod method (Blake and Hartge 1986), in which a dry clod of a known weight is coated with a water-repellent substance and the sample is weighed in water. Making use of Archimedes' principle, the volume of coated clod is calculated from the clod's water displacement (Saleh 1993; Grossman and Reinsch 2002). However, this method, which is time-consuming, needs clods 4-10 cm in diameter, which should be sufficiently stable for handling (Palmer and Troeh 1995). Removing the coating is difficult, tedious and subject to error. Clod density can also be estimated by an easier, less time-consuming method, which consists of saturating the porosity of small clumps or aggregates with kerosene before measuring the buoyant force in this liquid (Monnier et al. 1973). This technique is suitable for samples with low macroporosity, at least 15% clay and a silty skeleton; it can thus be used on several dozen aggregates at the same time (2-5 g soil), and is therefore a more representative sample than a single aggregate. Alternatively, clod density has been measured by gamma-ray attenuation (Benjamin and Cruse 1985) or with an automated, three-dimensional laser scanning technology (Rossi et al. 2008). Although these techniques arc non-destructive, the relatively high cost of the equipment has precluded widespread use. More recently, Stewart et al. (2012) used the photogrammetry (PHM) technique to estimate the bulk density of soil clods ranging in volume from 15 to 40 [cm.sup.3] (diameter 30-40 mm). This method, which has been successfully employed to characterise soil roughness (Taconet and Ciarletti 2007, 2010) and micro-relief (Aguilar et al. 2009), uses a standard digital camera that photographs a rotating clod, allowing reconstruction of its three-dimensional surface and subsequent calculation of its volume. Although this method has been successfully tested on medium to large soil aggregates, its feasibility on small aggregates has not been demonstrated.

With the lack of information concerning the bulk density of small soil aggregates, mainly due to the difficulties of measuring the volume of such small units, effort is needed to adapt the available methods to cover these issues. This study was designed to test whether PHM can be used to estimate the volume and subsequent bulk density of small soil aggregates (i.e. diameter 1-8 mm). The method was validated with respect to the Archimedes method in rough stones of diameter 1-16 mm. It was subsequently used to measure the volume and the corresponding bulk density of the small soil aggregates collected from two different soils under different tillage systems.

Material and methods

Experimental design

The volume of soil aggregates was determined by placing samples on a rotating imaging stand (Fig. 1a), which, as in the study of Stewart et al. (2012), includes a calibration object of known volume. In our case, the calibration object was a wood cylinder 6.04 mm in diameter and 10 mm high, with a red ring painted in the middle of the cylinder (Fig. 1b). The ring was subsequently used to scale the digitised aggregate. A 2-mm-thick transparent methacrylate disc was placed between the aggregate and the cylinder, and the rotation of the imaging set was powered up with an electrical motor. To improve precision of the aggregate-volume measurement, the flattest face of the aggregate was placed against the methacrylate disc surface. A single-board microcontroller (Arduino), which connects the motor to the camera and a computer, allowed control of the number of photos per rotation (Fig. 1a). The soil aggregate and the cylinder were photographed using a six-megapixel Nikon D80 camera with a 105-mm lens (Micro Nikkor 105 mm 1 :2.8G) (Nikon, Tokyo), and the photos were automatically sent and saved on the computer. Depending on aggregate size, the imaging stand was positioned 0.20-0.40 m from the camera focal plane. In total, 30 and 40 images per rotation were taken for aggregates >4 mm and <4 mm, respectively. The photo exposure time was ~3-4s and the minimum diaphragm aperture was selected. This means a total of 6 min per aggregate.

The photos were joined together by using PhotoScan software (Agisoft, St. Petersburg, Russia) (Fig. 2a). This software uses common points between photos to create three-dimensional point clouds of x, y, z- and r, g, b-referenced vertices. The reconstructed aggregate was converted into .ply (polygon) format and then it was manipulated using the freeware program MeshLab (http://meshlab.sourceforge.net/) (Fig. 2b). Within MeshLab, colour selection filters and manual removal of extraneous vertices were used to isolate the aggregate point clouds. Poisson surface meshes were used to reconstruct the aggregate. This technique considers all points at once without resorting to heuristic spatial partitioning or blending, and is therefore highly resilient to data noise. This approach allows a hierarchy of locally supported basis functions, and therefore the solution reduces to a well-conditioned sparse linear system (Kazhdan et al. 2006). PHM provides a cloud of points that need to specify an arbitrary scale. Scaling of the soil aggregate required the following steps. Using a chromatic analysis, the points of the red ring drawn on the wood cylinder were detected and located. These points were fitted to a circle using the least-squares method, and real dimensions of the diameter cylinder calculated. Once the aggregate was scaled, its surface and volume were numerically calculated. Since Poisson reconstruction provided a mesh made with a set of connected triangles, the aggregate surface was calculated as the sum of the areas of these triangles. To calculate the aggregate volume, the aggregate was adjusted to a cube, which was divided into 1000 x 1000 x 1000 small cubes. Making use of a discrete algorithm, the small cubes were successively occupied from the centre to the wall of the aggregate, and the volume was calculated as the sum of the small cubes contained within the surface of the aggregate. The 1000 x 1000 x 1000 partition was selected from a preliminary analysis of results convergence to obtain sufficient accuracy. This process, which could be simultaneously applied to different aggregates, took ~ 15 min per aggregate.

Method validation and testing

This method was validated by comparing the volume of rough stones measured by Archimedes' principle to the corresponding values estimated with PHM. Sixteen rough stones of different sizes (1-4, 4-8 and 8-16 mm diameter) were dried, weighed and immersed in distilled water. The stones were placed in a small receptacle immersed in water, and the volume was calculated according to Archimedes' principle. The water temperature was taken and the water density corrected. Next, the same stones were placed in the rotating imaging stand and their volumes estimated according to the above-described procedure.

The PHM method was subsequently used to estimate the volume of soil aggregates and the sensitivity of this method was evaluated for detecting differences in bulk density of aggregates from soils under different tillage systems. Soil samples (0-5 cm depth) were collected from two different, Penaflor and Torres de Alcanadre, both in the Zaragoza province of north-eastern Spain. Details on soil characteristics and management for each site are given in Blanco-Moure et al. (2012). Briefly, at Penaflor, soil sampling was carried out in research plots from a long-term tillage experiment at the dryland research farm of the Estacion Experimental de Aula Dei (Consejo Superior de Investigaciones Cientificas). Three tillage treatments were compared under the traditional cereal-fallow rotation of the area: conventional tillage with mouldboard ploughing (CT), reduced tillage with chiselling (RT), and no tillage (NT). A randomised complete block design with three replicates per tillage treatment was used. A composite sample from each of the three tillage plots per treatment (CT, RT and NT) was used. At the Torres de Alcanadre site (hereafter called Torres), the study was conducted under on-farm conditions (fields of collaborating farmers) where adjacent fields of NT and CT were compared under a continuous cereal cropping. In this case, undisturbed soil under native vegetation (NAT) close to the NT and CT fields was included in the study. Both soils were medium-textured soils (loam at Penaflor and sandy loam at Torres), alkaline (pH >8) and with low organic carbon content (OC <20 g [kg.sup.-1]) (Table 1). Soil samplings were made in three different zones within each field (CT, NT and NAT). Three soil samples were collected and mixed to make a composite sample. Once in the laboratory, soil samples were air-dried at room temperature (~20[degrees]C) and sieved to obtain aggregates of three different size classes (8-4, 4-2 and 2-1 mm diameter). Six aggregates per tillage treatment and size class were selected, weighed and subsequently placed on the rotating imaging stand to measure their volumes and surface areas. The bulk density of the aggregate was calculated as the quotient between the weight and its corresponding volume.

The soil aggregate shape was characterised with the dimensionless index:

E = 4[pi][([cube root of 3V/4[pi]).sup.2]/S (1)

where V and S are the volume and surface of the soil aggregate, respectively. This index, which indicates how close the aggregate form is to a sphere, ranges between 0 (plane) and 1 (sphere). For instance, the E value for a sphere, dodecahedron, cube or plan surface is 1.0, 0.91, 0.81 or 0.0, respectively. Within each study site, statistical comparisons among treatments were made by using one-way ANOVA, assuming a randomised experiment.

For the general characterisation of soils, particle-size distribution was obtained by laser diffraction analysis (Coulter LS230; Beckman Coulter, Brea, CA, USA), OC and CaC[O.sub.3] contents by dry combustion with a LECO analyser (LECO Corp., St. Joseph, Ml, USA), and electrical conductivity and pH by standard methods (Page et al. 1982).

Results and discussion

There was a strong correlation (Fig. 3) between the volume of the stones measured by using Archimedes' principle and that determined with PHM. This indicates that, unlike the reference clod method, which is applicable only to medium-large soil aggregates (4-10 cm diameter), this relative inexpensive method is sensitive enough to estimate the volume of soil aggregates in the size range 1-8 mm diameter.

The average [rho] value of soil aggregates determined by PHM ranged between 1.50 and 1.92 g [cm.sup.-3] (Fig. 4.). These results are comparable to those reported for other soils (Schafer and Singer 1976; Blanco-Canqui et al. 2005a; Stewart et al. 2012) and varied not only with soil management or tillage treatment but also with aggregate size. Although the differences among aggregate sizes were not always statistically significant, an increase in [rho] with decreasing aggregate size was observed in all cases (Fig. 4). The higher density of small aggregates has been reported in previous studies (Park and Smucker 2005; Blanco-Canqui et al. 2005a), and suggests that they have lower macroporosity after higher cohesion and more contact points than large aggregates. Soil management had a significant effect on [rho] (Fig. 4) with a general trend of increasing [rho] with the degree of soil alteration (i.e. NAT < NT < RT < CT). These results are in agreement with previous data of tensile strength of aggregates from these same soils (Blanco-Moure et al. 2012) and indicate that the highest [rho] of CT aggregates responds to tillage operations. Tillage disrupts soil aggregates, depleting soil OC, and causes rapid post-tillage consolidation. By contrast, in NT and, especially, NAT soils, the lower [rho] is due to higher biological activity, promoted by the minimal or no soil disturbance, which results in higher porosity and OC content (Blanco-Moure et al. 2012). No large differences were found between Penaflor and Torres, as expected from the similar soil texture (medium textures) and OC content (average 11.7 and 12.0 g [kg.sup.-1] for the agricultural soils of Penaflor and Torres, respectively). Considering together the two sites and all treatments, we found that 74% of the total variation in [rho] was explained by the aggregate size (d, diameter in mm) and the aggregate-associated OC (g [kg.sup.-1]) as follows:

[rho] = 2.33 + 0.298/d - 0.702 log OC ([r.sup.2] = 0.744, P < 0.0001) (2)

This relationship (Fig. 5) agrees well with that previously obtained for tensile strength of these same soils (Blanco-Moure et al. 2012), confirming the validity and applicability of the PHM method.

The E index averaged 0.864, ranging from 0.750 to 0.926. This indicates that aggregate shape varied between a dodecahedron and a cube form. The smallest aggregates showed a more spherical shape than the larger ones. No significant differences in E among tillage treatments were observed in Torres. However, the E index calculated in the aggregates from Penaflor was significantly lower under NT (Fig. 6). Overall, the standard deviation calculated for the E index was <6%, which indicates that the aggregate shape was quite homogeneous.

Conclusion

This paper demonstrates the viability of PHM to determine the volume of small soil aggregates, with size ranging from 1 to 8 mm in diameter. The method was validated by comparing the volume of stones measured by Archimedes' principle to the corresponding values estimated with PHM. Next, the method was used to estimate the bulk density of soil aggregates under different tillage treatments. The results demonstrated, likewise, that PHM was sufficiently sensitive to detect changes in aggregate bulk density in response to soil management and tillage.

Acknowledgements

The authors are grateful to Ruben Martin, M. Josefa Salvador and Ricardo Gracia for their help in various technical aspects of this study. The fellowship within this work has been supported by the Ministerio de Ciencia e Innovacion of Spain (BES-2011-076839).

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http://dx.doi.org/10.1071/SR15163

D. Moret-Fernandez (A,C), B. Latorre (A), C. Pena (A), C. Gonzalez-Cebollada (B), and M. V. Lopez (A)

(A) Departamento de Suelo y Agua, Estacion Experimental de Aula Dei, Consejo Superior de Investigaciones Cientificas (EEAD-CSIC), Avenida Montanana 1005, 50059 Zaragoza, Spain.

(B) Area de Mecanica de Fluidos, Escuela Politecnica Superior de Huesca--Universidad de Zaragoza, Carretera de Cuarte s/n. 22071, Huesca, Spain.

(C) Corresponding author. Email: david@eead.csic.es

Table 1. Selected properties of the studied soils in the 0-5cm depth

CT, Conventional tillage; RT, reduced tillage; NT, no tillage; NAT,
natural soil; OC, organic carbon; EC, electrical conductivity

Site        Treatment    pH([H.sub.2]O)     EC (1 : 5)      Sand
                           (1 : 2.5)      (dS [m.sup.-1])

Penaflor        CT            8.4              0.23         287
                RT            8.4              0.19         318
                NT            8.3              0.31         313

Torres de       CT            8.4              0.15         584
Alcanadre       NT            8.2              0.25         615
               NAT            8.4              0.15         695

Site        Treatment        Silt             Clay          CaC
                                          (g [kg.sup.-1])   [O.sub.3]

Penaflor        CT            463              250          462
                RT            439              243          466
                NT            451              236          473

Torres de       CT            281              135          235
Alcanadre       NT            269              116          223
               NAT            215               90          233

Site        Treatment         OC

Penaflor        CT            10.7
                RT            11.1
                NT            13.3

Torres de       CT            10.5
Alcanadre       NT            13.4
               NAT            16.8
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Author:Moret-Fernandez, D.; Latorre, B.; Pena, C.; Gonzalez-Cebollada, C.; Lopez, M.V.
Publication:Soil Research
Article Type:Report
Geographic Code:1CANA
Date:May 1, 2016
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