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Geoderma 141 (2007) 370 – 383
www.elsevier.com/locate/geoderma
Assessing the geochemical inherent quality of natural soils in the Douro river
basin for grapevine cultivation using data analysis and geostatistics
A.P. Reis b,c,⁎, L. Menezes de Almeida b , E. Ferreira da Silva b , A.J. Sousa a ,
C. Patinha b , E.C. Fonseca b
b
a
CERENA, IST, Technical University of Lisbon, Lisbon, Portugal
ELMAS - Investigation Unit, Geosciences Department, University of Aveiro, Aveiro, Portugal
c
PD grant, SFRH/BPD/27141/2006, FCT, Lisbon, Portugal
Received 9 March 2006; received in revised form 28 June 2007; accepted 3 July 2007
Available online 15 August 2007
Abstract
The main purpose of this study is to measure the spatial variability of a specific soil characteristic, its content of nutrients and toxic metals, and
use the established patterns of variability as an indicator of the inherent quality of the natural soils of the Douro river basin for grapevine
cultivation. A total of 108 topsoil samples were collect within the basin catchment. All soil samples were analysed for 26 chemical elements by
ICP-ES OPTIMA at ACME Anal. Lab. (ISO 9002 Accredited Co.). For this study, we selected only the chemical elements that are relevant for the
vineyards, that is, elements that are nutrients or those which are potentially toxic. In our study we used the following methodology: (i)
categorisation of quantitative variables and use of multiple correspondence analysis (MCA) to determine similarities among them; (ii) structural
analysis through variography to identify spatial patterns of variability; (iii) mapping of MCA factors through ordinary kriging to define, within the
basin, areas in which soils can be characterised according to their contents of nutrients or toxic elements. This methodology allows the
determination of spatial patterns of variability for groups of similar variables instead of an exhaustive “one-by-one” variable study, since data
analysis reduces the amount of information that must be interpreted in order to assess the natural aptness of the soils for grapes plantation.
© 2007 Elsevier B.V. All rights reserved.
Keywords: Douro River Basin; Soil; Geochemistry; Inherent quality; Vineyards; Data analysis; Geostatistics
1. Introduction
Located in Northeast Portugal, within the Douro River basin,
surrounded by mountains that give it very particular soil and
climacteric characteristics, the Port and Douro Wine Region is a
wide area of vineyards which as a strong social impact on the
region. Providing a key product for the national economy, this
area also offers a beautiful landscape and has a two-millennium
history (IVDP, 2006).
Due to the interest of this region, and in the scope of a
national wide soil geochemical survey, a decision was made to
use the soils of the Douro River basin to assess their quality for
grapevine cultivation. In this study, such quality should be
⁎ Corresponding author. PD grant, SFRH/BPD/27141/2006, FCT, Lisbon,
Portugal.
E-mail address: pmarinho@geo.ua.pt (A.P. Reis).
0016-7061/$ - see front matter © 2007 Elsevier B.V. All rights reserved.
doi:10.1016/j.geoderma.2007.07.003
regarded only in terms of a inherent quality, the soil's natural
ability to function for the purpose it is meant for.
Therefore, the main purpose of this study is to measure the
spatial variability of a specific soil characteristic, its content
of nutrients and toxic metals, and use the established patterns
of variability as an indicator of the inherent quality of the
natural soils of the Douro river basin for grapevine cultivation.
To achieve these objectives, a methodological sequence of
multivariate data analysis is here proposed. The aim is to
identify the main factors controlling the spatial variability,
using geostatistical techniques to interpolate such factors
(Goovaerts, 1999).
Principal Component Analysis, a multivariate data analysis
technique, has been extensively used in soil sciences (Jolliffe,
2002) for dealing with numerical data. This method is particularly
useful to identify linear relationships among variables. However,
assessing the soil quality for grapevine cultivation implies the use
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371
Fig. 1. Elevation map of the study area (adapted from DRAOT-Norte, 2001).
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Fig. 2. Geological map of the Douro River basin (adapted from DRAOT-Norte, 2001).
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373
Fig. 3. Soil sampling locations at the Douro River Basin.
of thresholds related to the physiological responses of the
grapevines. Therefore, in the present study we used Multiple
Correspondence Analysis after a previous categorisation of the
numerical variables using suitable thresholds.
Similar methodologies have been used in several fields of
science, from geochemical exploration of mineral deposits
(Jiménez-Espinosa et al., 1992; Reis et al., 2004) to environmental studies (Reis et al., 2005) or soil science (Wu et al., 2006).
The regional geological units of the basin are mainly heavily
deformed metassedimentary rocks (schists and quartzites)
intruded by granitoids (DRAOT-Norte, 1999). In the northeastern sector of the basin occurs the ophiolitic complex associated to some mafic and ultramafic rocks like peridotites,
2. Site description
Variable
Mean
Med.
Min.
1°Q
3°Q
Max.
Var.
Skew.
Cu
Pb
Zn
Ni
Co
Mn
Fe
As
Cd
V
Ca
P
Cr
Mg
B
K
24
28
75
28
12
508
2.97
28
0.24
33
0.12
0.065
31
0.47
2.55
0.34
19
24
69
21
10
351
3
18
0.2
27
0.07
0.055
23
0.35
2
0.27
1
4
14
2
1
16
0.58
2
0.2
4
0.01
0.012
3
0.04
2
0.04
12
20
54
8
4
190
2
13
0.2
17
0.02
0.038
12
0.2
2
0.13
29
31
83
34
16
602
3.81
36
0.2
40
0.19
0.078
35
0.73
3
0.45
111
171
344
539
55
3372
6.49
257
0.7
179
0.85
0.198
243
2.45
6
1.52
428
361
2034
2787
80
334,130
1.38
919
0.01
804
0.02
0.002
1169
0.17
0.68
0.08
2.0
4.7
3.9
8.6
1.5
3.2
0.4
4.5
2.9
2.6
2.3
1.5
3.3
1.9
1.6
1.7
The Douro River has its source in Spain, it runs
approximately two thirds of its course through Portuguese
territory and reaches the sea at the city of Oporto, north of
Portugal. In Portuguese territory, the basin has an area of
18,643 km2, with E–W orientation. The mountains surrounding
the Douro River basin, namely Gerês, Marão and Montemuro,
determine its particular climate. Fig. 1 shows the elevation map
of the study area. Such geomorphology results on the
dominance of two climate types over the basin. In the west,
the climate is strongly influenced by the sea, being humid
(average annual rainfall ranges from 1200 to 2500 mm) and
with mild temperatures (average annual temperature ranges
from 11 to 15 °C). The eastern sector has a more continental
climate, with a larger temperature range (cold winters and hot
summers), and is dryer than the west sector (average annual
rainfall ranges from 400 to 1200 mm).
Table 1
Summary statistics for sixteen geochemical variables
Cu, Pb, Zn, Ni, Co, Mn, As, Cd, V, Cr and B expressed in mg/kg; Fe, Ca, P, Mg
and K expressed in %.
Med. — Median; Min. — Minimum; 1°Q — 1st quartile; 3°Q — 3rd quartile;
Max. — Maximum; Var. — Variance; Skew. — Skeweness.
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Fig. 4. Classed post maps (point maps) for Ni and Pb. The limits of the classes correspond to background values, deficiency and toxicity standards.
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Table 2
Upper limits for the concentration classes defined using soil standards
Cu_mg/kg
Zn_mg/kg
Co_mg/kg
Mn_mg/kg
As_mg/kg
V_mg/kg
Ca_%
P_%
Cr_mg/kg
Mg_%
K_%
a
Deficiency a
Optimum a
Normal a
Excess a
Toxicity a
5
15
5
20
–
–
0.5
0.1
–
0.20
1
–
50
–
352
–
–
–
–
–
–
–
28
300
50
1500
20
50
3.57
0.57
100
1.22
5.77
100
–
–
N 1500
–
–
N 3.57
N0.57
–
N 1.22
N5.77
N100
N300
N50
–
N20
N50
–
–
N100
–
–
Kilby, 1998; Reiman and Caritat, 1998.
375
Table 4
Eigenvalues and percentages of the total inertia of the cloud for each of the four
first factorial axes resulting from MCA
Factorial axis
Eigenvalue
% Total inertia
% Total inertia (cumulative)
F1
F2
F3
F4
0.114867
0.052459
0.025032
0.015678
41.77
19.08
9.10
5.70
41.77
60.85
69.95
75.65
(iv) Regosols — normally correspond to downhill colluvial
deposits.
With regard to the general physical and chemical characteristics, these soils are poor in organic matter, predominantly acid
amphibolites and gabros (massif of Morais and Bragança).
Fig. 2 shows the geological map of the Douro River basin.
As far as the origin of the soil is concerned, most of the soils
within the basin derived from metassedimentary rocks and
granitoids, being the vineyards soils of schistose origin. There
are two main soil types in the basin:
I. Soil that has been greatly affected by anthropogenic
actions, being strongly altered both chemically and morphologically. This soil can be found in vines and it can be classified
as an anthrosol, according to FAO-UNESCO (1988). Such soil
has not been sampled and is not considered in this study.
II. Soils affected by less aggressive anthropogenic activities
where the geogenic signature is still recognizable. These undisturbed soils were used in this study. The most representative
types within this group are (DRAOT-Norte, 1999; Galopim de
Carvalho, 2003):
(i) Leptosols — a generally thin layer of soil (less than 30 cm),
slightly acid and poor in organic matter, with an efficient
drainage system and a high vulnerability to erosion.
(ii) Cambisols — a 50 to 100 cm layer of distric soil, poor in
organic matter, fairly well drained and with low
vulnerability to erosion.
(iii) Anthrosols — a 50 to 100 cm layer of aric soil, poor in
organic matter, well drained and with low vulnerability
to erosion.
Table 3
Codes for the categories used in multiple correspondence analysis
Cu
Zn
Co
Mn
As
V
Ca
P
Cr
Mg
K
Deficiency
Optimum
Normal
Excess
Toxicity
Code 1
Code 2
Code 3
Code 4
Code 5
Cu1
Zn1
Co1
Mn1
–
–
Ca1
P1
–
Mg1
K1
–
Zn2
–
Mn2
–
–
–
–
–
–
–
Cu3
Zn3
Co3
Mn3
As3
V3
Ca3
P3
Cr3
Mg3
K3
Cu4
–
–
Mn4
–
–
–
–
–
Mg4
–
Cu5
Zn5
Co5
–
As5
V5
–
–
Cr5
–
–
Fig. 5. Categories projections on the factorial planes F1/F2, F1/F3 and F1/F4.
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Fig. 6. Sample variograms of F1 (major and minor axes for each of the two spherical structures), F2 and F3 (major and minor axes for the single spherical structure)
calculated along the main directions of the corresponding ellipse. The bold line matches the theoretical model fitted to the sample variogram.
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377
Table 5
Parameters of the geometrical anisotropic spherical variogram models fitted to the first three axes of ACM
Variable
C0
C1
Range
Direction
Anisotropy
C2
Range
Direction
Anisotropy
F1
F2
F3
0.05
0.06
0.00
0.1
0.17
0.158
33,000
47,000
60,000
N35°E
N35°E
N10°W
1.7
1.3
1.5
0.19
–
–
73000
–
–
N10°W
–
–
1.6
–
–
C0 — nugget effect; C1 — sill for the 1st spherical structure; C2 — sill for the 2nd spherical structure; Range — major range for each spherical structure in meters;
Direction — major axis orientation for the ellipse of each spherical structure; Anisotropy = major axis/minor axis.
(pH between 4.6 and 6.0) and have a dominant mixed clay/lithic
matrix.
As regards the use of the soil, 37% of the basin is occupied
by forest, 41% is used for heterogeneous agriculture, 9% for
vineyards and orchards, 8% for pastures and only 1% of the
basin consists of artificial lands (DRAOT-Norte, 1999).
Such distance can be graphically displayed projecting the
categories on the factorial axes. Multiple Correspondence
Analysis (MCA) can be seen as an extension of CA to more
than two categorical variables.
3. Methods
Variography provides a description of the spatial pattern of a
continuous attribute Z. Given a data set for the variable Z at n
locations xi, (Z(xi), i = 1, 2, …, n), the sample variogram γZ⁎(h) –
the symbol ⁎ in this text will indicate estimates – measures the
average dissimilarity between data separated by a vector h
(Goovaerts, 1999),
3.1. Sampling, sample preparation and chemical analysis
As shown in Fig. 3, 108 topsoil samples were collected within
the basin. Due to the purpose of this work (assessing the inherent
quality of the soil for cultivation), sampling locations were
selected in order to avoid any natural or anthropogenic disturbance of the soil, or natural enrichments such as mineralisations. Composite samples, made up of 5 sub-samples, were
collected over an area of approximately 100 m2, at a maximum
depth of 15 cm. The organic layer (usually a thin cap of vegetation) was previously removed (Menezes de Almeida, 2005).
The b 170 μm soil fraction, obtained by dry sieving, was
taken for chemical analysis. A 0.5 g split of the soil samples was
leached in hot (95 °C) aqua regia (HCl–HNO3–H2O) for 1 h.
After dilution to a final volume of 10 ml with distilled water, the
solutions were analysed by ICP-ES OPTIMA at ACME Analytical Laboratory (ISO 9002 Accredited Co.) for Bi, Mo, Sb,
Cu, Pb, Zn, Ni, Co, Mn, Fe, As, Th, Sr, Cd, V, Ca, P, La, Cr, Mg,
Ba, Ti, B, Al, Na, K, W and Hg. In order to assess the validity of
the analytical results, accuracy and precision (repeatability)
were calculated. The results have shown that the accuracy of the
analytical procedure was usually inferior to 7.5%, with the
exceptions of As and B (Er≅12%), while the precision was
≅5% for all elements, with the exceptions of B and Pb.
3.3. Variography
g⁎Z ðhÞ ¼
1 X
½ Z ð x i Þ Z ð x i þ hÞ 2
2N ðhÞ i¼1
N ðhÞ
ð1Þ
where N(h) is the number of data pairs at a lag of h.
The variogram can be calculated for different directions of h,
which allows us to know how the variable Z(x) varies in several
directions of the space.
3.4. Ordinary kriging
The main application of geostatistics to soil science has been
the estimation and mapping of soil attributes in unsampled
areas. Kriging is a generic name for a family of least-squares
predictors. For the prediction of the variable Z at a location x0,
{Z(x0)}, the estimator Z⁎(x0) is defined as (Goovaerts, 1999):
Z ⁎ðx0 Þ ¼
n0
X
ki Z ð x i Þ
ð2Þ
i¼1
3.2. Data analysis
where n0 is the number of sample neighbours and the λi are
weights found by solving the system of equations,
Correspondence Analysis (CA) is a factor analysis method
that uses categorical (or discrete) variables. It was designed to
describe a two-way contingency table N (Benzécri, 1980;
Greenacre, 1984), in which we find, at the intersection of a row
and a column, the number of individuals that share the characteristic of the row and that of the column. Like other factorial
methods, CA aims to reduce the number of variables and to
detect a structure in the relationships between the variables. CA
defines a measure of distance (or association) between two
points, such points being the categories of the discrete variables.
Benzécri (1980) used a distributional distance known as the x2.
f
n0
X
kj g xi ; xj þ l ¼ gðxi ; x0 Þ; i ¼ 1; N ; n0
j
n0
X
kj ¼ 1
ð3Þ
j
with γ (h) being the theoretical model for the variogram of the
variable Z (fitted to the sample variograms) and μ being a
Lagrange multiplier.
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4. Results
4.1. Exploratory data analysis
According to its effects on the grapes, we have considered
three types of chemical elements:
(i) Those that are nutrients for the grapes (P, K, Mg, Fe, B
and Mn);
(ii) Those meaning toxicity for the grapes (Pb, Ni, Cr, Cd, As
and V);
(iii) Nutrients which become toxic when existing in high
concentrations in the soil (Ca, Co, Cu and Zn).
The concentrations of the sixteen chemical elements in 108
soil samples are the continuous variables used to assess the
inherent quality of the soil.
To begin with, some simple statistical parameters were
calculated to describe these continuous variables. The results
are shown in Table 1. As indicated by the skewness coefficient,
the distribution of the data for Pb, Zn, Ni, or As is highly
positively skewed due to the presence of a few large values, that
were statistically classified as outliers. Therefore, the use of
parametric techniques like ordinary kriging can produce poor
results, strongly influenced by the number of samples used in
each prediction, which is a severe shortcoming when dealing
with low-density sampling surveys. Additionally, the influence
of these outliers and skewness can endanger the spatial continuity of the variogram function. The removal of the outliers
from the data matrix does not seem reasonable if the intention is
to work with toxicity levels (the outliers probably match the
samples with toxic levels of these metals). Several solutions
have been used to deal with this kind of variables based on some
data transformations (indicator, logarithmic, or some other
normal-score transform). The approach used in this study was
the indicator transform obtained by discretisation of the
continuous variables using meaningful thresholds. The new
attributes produced did not have a quantitative input but a
qualitative one.
Maximum values presented in Table 1 also show that the
maximum concentration for Cd (0.7 mg/kg) is lower than the
toxicity level [1.5 mg/kg (Reiman and Caritat, 1998)]. Such
metal has no known function in plant metabolism and only acts
as a toxic substance if assimilated in high amounts. Since Cd
concentrations in these soils are not within toxic levels, the
element was excluded from this study.
Table 1 also shows that Fe concentrations (minimum of
5800 mg/kg) are well above minimum acceptable values for a
normal growth of the wine grapes (b50 mg/kg according to
Kilby, 1998), meaning that Fe deficiency is not a concern for
these soils. An inverse result was found for B, which has a
maximum value of 21 mg/kg. Kilby (1998) proposes a
deficiency threshold of 21 mg/kg, meaning that the soils of
the Douro River basin are naturally poor in B.
Previously to more complex mapping, classed post maps
were done for the remaining thirteen geochemical variables.
Such maps provided relevant information regarding the spatial
point distribution of some geochemical variables. Fig. 4 gathers
Fig. 7. Map of F1, the first factorial axis produced by MCA.
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379
Fig. 8. Map of F2, the second factorial axis produced by MCA.
the relevant maps and shows that, with one exception, concentrations of Ni and Pb in the soil are always below the toxicity
level, corresponding to 210 mg/kg (Swartjes, 1999) and 100 mg/
kg (Reiman and Caritat, 1998), respectively, meaning that
natural concentrations of these heavy metals do not reach hazardous values in this basin.
This exploratory analysis resulted in the removal of Cd, Fe,
B, Ni and Pb from the statistical analysis.
4.2. Categorisation of the geochemical variables
To categorise the geochemical variables it was necessary to
divide them into classes of concentrations, limited by standards
stated in scientific literature. According to the nature of the
geochemical variable (nutrient or toxic heavy metal) several
types of standards were used:
1. Background values for Portuguese soils (Inácio, 2004);
2. Plant-specific standards for grapevine cultivation (Kilby,
1998);
3. Maximum tolerable level for agricultural soils (Reiman and
Caritat, 1998) whenever existent;
4. Potential-risk standards for soils (Swartjes, 1999) if
standards for agricultural soils were not available.
Therefore, categorisation involved plant-specific standards
for nutrients and maximum tolerable values for agricultural
soils, or potential-risk standards for heavy metals. Five
categories were then defined: Deficiency, Optimum, Normal,
Excess and Toxicity (codes 1, 2, 3, 4 and 5, respectively).
Note that the Optimum category is a sub-class of the Normal
category, defined only for Zn and Mn (elements with thresholds
defined for an Optimum category), representing a narrower
concentration interval for which there's the most favourable
plant growth. In this sense, the definition of such a category was
just a refinement to obtain a more detailed soil classification.
Refinements for the remaining classes were not done due to the
lack of more specific soil standards.
The different character of the several geochemical variables (nutrient, toxic or both) resulted in a different number of
categories for each variable, since some elements are necessary
for the plant, while others do not play any role in its metabolism. Table 2 shows the soil standards used to establish the
Table 6
Parameters of the geometrical anisotropic spherical variogram models fitted to
the soil properties
Variable
C0
C1
Range
Direction
Anisotropy
Cu
Zn
Co
Mn
As
V
Ca
P
Cr
Mg
K
10
250
20
80,000
10
100
80
1.75
60
500
100
415
1755
59
250,000
900
695
122
15.2
1100
1150
720
65,000
50,000
79,000
80,000
55,000
55,000
63,000
32,000
42,000
60,000
30,000
N10°W
N35°E
N10°W
N35°E
N55°W
N10°W
N10°W
N35°E
N55°W
N10°W
N55°W
1.90
1.25
1.80
1.60
1.50
1.30
1.30
1.50
1.10
2.90
1.20
C0 — nugget effect; C1 — sill for the 1st spherical structure; C2 — sill for the
2nd spherical structure; Range — major range for each spherical structure in
meters; Direction — major axis orientation for the ellipse of each spherical
structure; Anisotropy = major axis/minor axis.
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Fig. 9. Geochemical maps of Cu, Ca, Co and Cr, for the natural soils of the Douro river basin.
different concentration classes. Table 3 shows the resulting
categories (for each geochemical variable) that were used to
run MCA.
it was necessary to define theoretical models of spatial variability for the new variables, the MCA factorial axes.
4.4. Variography
4.3. Multiple correspondence analysis (MCA)
From the ten factorial axes (or axes of inertia) produced by
the analysis, we have considered the first four, which account
for about 75% of the total inertia of the cloud. Table 4 shows the
inertia accounted by each axis and Fig. 5 displays the categories
projections on the first three factorial planes.
The results show that the 1st factor (F1) opposes the lower
concentrations (Deficiency and Optimum categories) to the
higher concentrations (Excess and Toxicity categories), whilst
the 2nd factor (F2) clearly separates average values (Normal
category) from the extreme ones (Deficiency, Optimum, Excess
and Toxicity categories). The 3rd factor (F3) detaches toxic
levels of Cu from those of other heavy metals, and the 4th factor
(F4) opposes high levels of Co, Cu, Zn and Mn to high levels of
V, Mg and Co.
To define inherent quality indicators of natural soils using
spatial patterns of distribution, for nutrients and toxic elements,
In order to account for the spatial structure, the variograms
were calculated for the directions N80°E, N35°E, N10°W and
N55°W. Such directions were selected based on the regional
geology of the basin. A theoretical model of spatial variability
was fitted to the sample variogram.
Fig. 6 displays some directional variograms for F1, F2 and
F3. Despite the fact that four directional variograms were
calculated for each variable, only those along the minor and
major directions of the ellipses formed by the geometric
anisotropies are shown (the existence of two structures for F1
lead to the display of the variogram along four directions).
Table 5 shows the parameters of the models fitted to the sample
variograms. The spatial model of variability for F1 shows three
components of continuity, a nugget effect and two spherical
structures with a geometric anisotropy, while the model for F2
has two components of continuity, a nugget and a spherical
structure, also with a geometric anisotropy. The spatial model for
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381
Fig. 10. Geochemical maps of P, V, Mn and Mg, for the natural soils of the Douro river basin.
F3 shows only a single component of variability, a spherical
structure with a geometric anisotropy, meaning that for F3 the
variations at a micro-scale (sample scale) are insignificant.
4.5. Maps of MCA factors
(ii) Associated to the negative semi-axis of F1 are the Deficiency and Optimum categories, meaning that negative
coordinates normally represent a deficiency in some
nutrients like Zn, Cu, Mn, Mg and Co. The negative
anomalies (light grey) are probably spatially related with
the granitoids which are common within the basin.
The theoretical models of spatial variability fitted to the
sample variograms (Table 5) were used to estimate, by ordinary
kriging, the categories coordinates along the factorial axes, at
unsampled locations.
Fig. 7 shows the map for F1. Bearing in mind the results of
MCA (projections on the first factorial plane, Fig. 5), the spatial
distribution of F1 shows that:
According to these results, the elemental concentrations in
the soils seem to be naturally controlled by the regional geology
of the basin.
Fig. 8 shows the spatial distribution of F2. Once again, we
need to consider the results of MCA (projections on the first
factorial plane, Fig. 5). The spatial distribution of F2 shows that:
(i) Associated to the positive semi-axis of F1 are the Excess
and Toxicity categories of the variables (Zn and As are the
exceptions). This shows that positive coordinates (corresponding to high concentrations of Co, Cr, Mg, Cu, V and
Mn) occur mostly on the soils of the NE sector of the
basin. Such high concentrations (dark grey areas) seem to
be spatially related to the ophiolitic complex and its
associated mafic and ultramafic rock (Figs. 2 and 7).
(i) With positive coordinates (dark grey areas) at F2 (except
for Ca), we have the extreme values, that is, the Deficiency,
Optimum, Excess and Toxicity categories, while with
negative coordinates (light grey areas) we have the Normal
category. The spatial distribution of F2 shows a NE–SW
trend that seems to be similar to the relief of the basin, with
the extreme categories distributed along the high relieves
and the normal concentrations along the low ones.
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Fig. 11. Geochemical maps of Zn, As and K, for the natural soils of the Douro river basin.
(ii) Zinc and arsenic are the exceptions, showing high
concentrations associated to normal concentrations of
the other elements.
The spatial distribution of F3 and F4 has a strong geologic
character, with both axes clearly related with the ophiolitic
complex and its associated mafic/ultramafic rocks. As the
understanding of the geological processes was not the aim of
this study, maps of F3 and F4 are not included in the paper.
4.6. Geochemical maps
Since the results of MCA were not completely clear about
which natural phenomenon controls the spatial distribution of
which element, we decided to investigate the spatial distribution
of each soil property separately.
In order to do so, experimental variograms were computed
for several directions and a theoretical model of spatial variability was fitted to the sample variogram. Table 6 shows the
parameters of the spherical models (with geometrical anisotropies) fitted to the variograms. These models of spatial
variability were used to estimate values of the soil properties, at
unsampled locations, by ordinary kriging (Goovaerts, 1999).
Figs. 9, 10 and 11 show the regional distribution maps for
the 11 geochemical variables. The contour levels used equal the
minimum, 1st quartile, median, 3rd quartile and maximum
values of each data population. The geochemical elements in
Fig. 9, Cu, Ca, Co and Cr, show a spatial pattern similar to that
of the regional geology, since the high values at the NE of the
basin match the ophiolitic complex and its associated mafic and
ultramafic rocks. A similar pattern of spatial distribution can be
seen in Fig. 10 for V, Mn and Mg, while K shows a different
distribution that cannot be related to the geology. With the
exception of Ca, such results are identical to those obtained with
the maps of MCA factors; V, Mn, Mg, Cu, Co and Cr associated
to F1 that has a spatial distribution controlled by the geology
(Fig. 7). For Ca, the interpretation of MCA results was harder,
due to the low number of classes of the variable (two classes
only, Ca1 associated to F2 and Ca3 associated to F1). However,
its spatial distribution is similar to those of the previous variables and we can assume that the contents of Ca in the soil are
also geogenic. Different spatial distributions were obtained for
P, Zn, As (Fig. 11) and K, but such distributions do not seem to
be related to the regional geology. However, Zn and P have a
NE-SW trend, quite similar to the image of F2. Similarly to Ca,
it was more difficult to interpret K and As, probably due to the
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A.P. Reis et al. / Geoderma 141 (2007) 370–383
low number of classes used in the MCA for these variables.
Nevertheless, it is quite probable that other phenomena besides
regional geology or geomorphology can control the distribution
of these elements, and such small scale phenomena were probably undetected by the regional survey.
5. Conclusions
This study focuses essentially on the applicability of some
statistical methods to assess the inherent quality of the natural
soils for grapevine cultivation, in the Douro river basin, or in a
broader approach, to assess some quality parameters based on
critical standards. The results show that:
(i) The geochemistry of the basin is related to the regional
geology, meaning that atmospheric deposition of metals
and metalloids is small. This assumption is supported by
the MCA (spatial distribution of F1) and by the
geochemical mapping of Co, Cr, Mg, Cu, Ca, V and Mn.
(ii) There seems to exist a certain control of the topography
over the distribution of element's concentrations, probably related to the regional geology (and tectonic), but such
hypothesis is only suggested by the similitude between
the relief and the patterns of spatial distribution of F2, Zn
and P. The results of MCA are not conclusive, perhaps due
to the low number of classes used for some variables.
However, additional thresholds suitable for the categorisation of such variables are not presently available.
(iii) The soils of the basin are naturally deficient in boron. This
result is not totally unexpected, since acid soils with light
textures under humid climates usually have low contents
of boron (Queijeiro et al., 2004). However, grapevines are
extremely sensitive to boron deficiencies, an essential
micronutrient particularly, during the early stages of growth.
(iv) The soils in the Northeast sector have high concentrations
of Co, Cr, Mg, Cu, V and Mn, probably related to the
ophiolitic complex and its associated mafic/ultramafic
rocks. From these, Cr, Cu, Co and V are potentially toxic
to the grapes, and Mn and Mg inhibit the normal growth
of the plant when excessively concentrated in the soil. A
common symptom of Mn excess is the development of
black, glossy dots on the grape berries, due to the
accumulation of Mn oxides (Menezes de Almeida, 2005).
(v) Soils related to the granitoid units are deficient in some
nutrients, like Zn, Cu, Mn, Mg and Co. Consequences
of the deficiency in these nutrients are an early falls of the
leaves or a reduction in berry volume (Menezes de
Almeida, 2005).
Acknowledgements
We wish to thank the reviewers for their helpful remarks. We
are also extremely grateful to Dr. Denise Terroso and Dr. Susana
Senos for their thorough revision of the final manuscript.
383
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