SOIL PROPERTIES, CONDITION AND SOIL LOSSES FOR SOUTH AND EAST BRAZILIAN FOREST AREAS
Norton, 1994; Six et al., 2000a; Denef & Six, 2005; Ruiz-Vera & Wu, 2006;
Norton et al., 2006), soil organic matter (Six et al., 2000a; Denef & Six,
2005), sodium adsorption ratio (Levy et al., 2003; Ruiz-Vera & Wu, 2006),
antecedent moisture content (Reichert & Norton, 1994; Lado et al., 2004;
Ruiz-Vera & Wu, 2006; Mamedov et al., 2006), Fe- and Al-oxides
(Pinheiro-Dick & Schwertmann, 1996; Ferreira et al., 1999; Muggler, et al.,
1999; Ajayi et al., 2009), polyacrylamide (PAM) molecular weight
(Mamedov et al., 2007), redox potential (De-Campos et al., 2009) and others
not related also can affect aggregate stability. Thus, the interaction of soil
chemical and physical properties suggests that aggregate stability is a
complex function (Levy & Mamedov, 2002; Levy et al., 2003). In addition,
soil management and type of vegetation also can change soil aggregation,
because of different organic compounds that are deposited in the soil.
The objective of this study was: (i) determine the main soil
properties for representative soil classes from different eucalyptus growing
regions of Brazil, and (ii) evaluate the relationship between aggregate
stability and soil properties for Brazilian areas under eucalyptus plantation.
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4 MATERIAL AND METHODS
4.1 The Study Area and Soil Description
The areas chosen for this study represent the dominant soils used for
eucalyptus cultivation in three Brazilian regions (Espírito Santo, Minas
Gerais and Rio Grande do Sul states). In the soil description below, soil
classifications are according to Embrapa (2006), and soil classifications
according to Soil Survey Staff (1999), the latter appearance in parenthesis.
In the Espírito Santo state the soils selected were classified as
dystrocohesive Yellow Argisol – PA1 (Hapludult), moderately rocky Yellow
Argisol – PA2 (Hapludult), and dystrophic Haplic Plinthosol – FX
(Phinthaquox), which represent more than 80% of soils from Coastal Plain
region. In the Rio Doce Valley, center-east region of Minas Gerais state, the
samples were collected in dystrophic Red-Yellow Latosol – LVA
(Haplustox) and dystrophic Red Latosol – LV (Haplustox), which are the
main soils there. In the south of Brazil the area encompasses eutrophic Red
Argisol – PVe (Rhodudalf), dystrophic Red-Yellow Argisol – PVA
(Hapludult), the main soils in Rio Grande do Sul state, and dystrophic Haplic
Cambisol – CXbd (Dystrudept) which represents the shallow soils there.
Espírito Santo soils were developed from tertiary age sediments above
Precambrian crystalline rocks (Brasil, 1970); in Minas Gerais they were
developed from granitic gneisses from Precambrian time (Celulose Nipo
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Brasileira - Cenibra, 2001); and Rio Grande do Sul soils were formed from
younger parent material, sediments form the tertiary-quaternary and
quaternary period (Ramgrab, 1997), being considered lesser developed
pedogenetically.
4.2 Soil Analyses
Soil samples were taken from the A horizons from the soils
described above. The samples were characterized for particle size
distribution using the hydrometer method (Gee & Or, 2002); it was also
determined bulk density (Blake & Hartge, 1986a), particle density (Blake &
Hartge, 1986b), total porosity (Danielson & Sutherland, 1986), cationexchange
capacity (CEC) using sodium acetate procedure, organic matter
(OM) content by potassium dichromate oxidation and ferrous sulfate
titration (Walkley & Black, 1934), iron extracted by dithionite-citratebicarbonate
(Fed) (Mehra & Jackson, 1960), ammonium oxalate (Feo)
(Schwertmann, 1964), and sulfuric attack (Fes) according to Embrapa
(1997), through sulfuric attack we also extracted silicon, aluminum, titanium
and phosphorus. The molecular ratio SiO2/Al2O3 and SiO2/(Al2O3 + Fe2O3)
was calculated according to Vettori (1959) and Embrapa (1997), using Si, Al
and Fe from sulfuric attack extraction. The X-ray diffraction (XRD) was
performed on a Siemens D500 diffractometer, with a generator settings of 40
kV and 35 mA and CoKα radiation (1.7890 Å). The mean geometric
diameter (MGD) of stable aggregates was determined by wet sieving
19
(Kemper & Rosenau, 1986) of air-dried aggregates for size from 4.75 to 8.00
mm. The soil losses (AUSLE) and erodibility (K factor) were obtained by
USLE-plots from previously studies performed by Martins (2005), Oliveira
(2006), and Oliveira (2008).
4.2.1 High-Energy Moisture Characteristic (HEMC)
a) Technique
The high-energy moisture characteristic (HEMC) method was first
proposed by Childs (1940), later modified by Collins-George & Figueroa
(1984), Pierson & Mulla (1989), and finally by Levy & Mamedov (2002). In
this method, the wetting process of the aggregates is accurately controlled,
and the energy of hydration and entrapped air are the only forces responsible
for aggregation breakdown. According to previous studies, the HEMC has
been reported as a useful method for determining of aggregate stability of
arid and humid zone soils with different stability levels (Pierson & Mulla,
1989; Levy & Miller, 1997; Levy & Mamedov, 2002; Levy et al., 2003;
Norton et al., 2006), and also has been noted for its ability to detect small
differences in aggregate stability (Pierson & Mulla, 1989).
The procedure is based on the following main steps. Aggregates
were wetted either slowly and rapidly in a controlled manner, and a soil
moisture content (MC) curve at high energies is constructed (Figure 1). An
index of aggregate stability was obtained by quantifying differences in MC
curves between fast and slow wetting (Figure 2a). For a given wetting rate, a
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structural index (SI) is defined by Collis-George & Figueroa (1984) using
the following express:
VDP
SI Eq. 1
MS
where VDP is the volume of drainable pores, and MS is the modal suction.
FIGURE 1 High-energy moisture characteristic (HEMC) apparatus.
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FIGURE 2 Schematic representation of (a) moisture release and (b) specific
water capacity curves for fast and slow wetting. The dashed line
in the specific water capacity curve represents soil shrinkage line
for slow wetting.
22
The modal suction corresponds to the mactric potential ( , J kg-1) at
the peak of the specific water capacity curve (d /d ), where is the water
content (kg kg-1) (Figure 2b). The VDP is the integral of the area under the
specific water capacity curve and above the dotted baseline (Figure 2b). The
dotted baseline represents the rate of water loss due to aggregate shrinkage
rather than pore emptying (Collis-George & Figueroa, 1984).
As aggregate slake and the pore sizes distribution changes, the
modal suction increases and the volume of drainable pores decrease. These
changes cause the value of the structural index decreases (Pierson & Mulla,
1989). Figure 2 shows typical changes in the modal suction value and the
volume of drainable pores.
The stability ratio (SR) value was also calculated for each soil
sample using Equation 1 and the following express (Pierson & Mulla, 1989):
SR SIfast wet Eq. 2
SIslow wet
The stability ratio was used to compare the resistance of aggregates
to slaking on a relative scale from zero to one. Since the SR is a
dimensionless value, soil samples of different size fractions and soil types
can be compared if identical wetting rates and sample handling procedures
are used for each sample (Pierson & Mulla, 1989).
b) Procedure
It was used sieved soil with a size of 0.5-1.0 mm. Fifteen grams airdried
aggregates were placed in a 60 mm i.d. funnel (Figure 1) to form a
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thick bed of about 5 mm (Collis-George & Figueroa, 1984; Levy &
Mamedov, 2002), which it provides accurate and quick results (Pierson &
Mulla, 1989). The fritted disk had a nominal maximum pore size of 20 to 40
µm. Saturation of the fritted disk was ensured prior to placing aggregates in
the funnel. The funnel was connected from its bottom via a tubing to a
peristaltic pump (Figure 1), which was then used to wet the aggregates in the
funnel either fast (100 mm h-1) or slowly (2 mm h-1). At the end of wetting,
aggregates were covered by standing water to ensure saturation (Levy &
Mamedov, 2002). In order to obtain water closest to the rainfall water
quality, distilled water was used for wetting the aggregates in the funnel.
Once the aggregates had been saturated (either slowly or rapidly), a
MC curve ( = f( )), at a matric potential ( ) range of 0 to -3.0 J kg-1, was
obtained using a hanging water column (Figure 1), whereby height of the
meniscus in the pipette was decreased in increments of 0.1 to 0.2 J kg
-1,
thereby increasing the suction applied. Volume of water that drained from
the aggregates at each matric potential was recorded after 2 min of
equilibrium period and corresponding water content of the aggregates was
calculated (Levy & Mamedov, 2002). Each treatment was duplicated.
c) Data analyses
To accurately calculate VDP and MS, modeling of MC curves was
carried out with the following seven-parameter modified van Genuchten
model (Pierson & Mulla, 1989).
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Aψ Bψ C
2
1 αψ n
1 n 1
θ Eq. 3
θr θs θr
where s and r are “pseudo” saturated and residual gravimetric water
contents, respectively; and control location and steepness of the S-shape
inflection of the MC curve, respectively; and A, B, and C are the quadratic
terms added by Pierson & Mulla (1989) to improve fitting of the model to
the MC curve. The term pseudo was added to saturated and residual water
contents owing to modification of the original van Genuchten model (van
Genuchten, 1980). Values of s and r can no longer be physically
interpreted in terms of saturated and residual water contents (Pierson &
Mulla, 1989).
Specific water capacity curve (d /d ), needed for obtaining the
value of modal suction, was computed by differentiating Equation 3 with
respect to matric potential, and had the explicit form:
d θ dψ
n
ψ 1
θs
αψn
θr
1
2Aψ
αψ
n
B
1 n
1
1
n
1
αψ n
Eq. 4
The VDP, that is, the area under the specific water capacity curve
and above the soil shrinkage like (Figure 2b), was calculated by subtracting
the terms for pore shrinkage (2A + B) from Equation 4, and analytically
integrating the reminder of that equation.
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5 RESULTS AND DISCUSSION
The soil clay mineralogy results were separated by region (Figures
3, 4, and 5). Within each region, soil mineralogy did not show great
differences among the profiles. This would be expected because in each
place the soil did not differ very much in pedogenetic development and the
parent material was the same. The XRD patterns showed kaolinite as the
dominant crystalline mineral for all soils (Figures 3, 4, and 5), which is the
most abundant clay mineral at Brazilian soils (Kämpf & Curi, 2003). The
diffractograms obtained for Espírito Santo soils (Figure 3) also contained a
small amount of goethite, quartz, anatase, rutile and HIV (hydroxyinterlayered
vermiculite). Differential thermal analysis performed by Duarte
et al. (2000) in soils from that region indicated that those soils have a
kaolinitic matrix with around 85%; with gibbsite contributions (around 5%);
and small amounts of quartz, anatase, and HIV. The analyses also showed
goethite as dominant Fe-oxide (Duarte et al., 2000). The soil mineralogical
composition from Rio Grande do Sul, obtained by XRD patterns (Figure 4),
was kaolinite, quartz, goethite, hematite, and HIV. However, HIV was not a
common clay mineral for all of these soils. Unlikely, XRD analyses pointed
out gibbsite for the soils from Minas Gerais (Figure 5), which also contained
kaolinite, goethite, and hematite. The remarkable presence of clay minerals
1:1 and Al- and Fe-oxides suggest occurrence of more weathering in these
26