Field-scale variability of topsoil dehydrogenase and cellulase activities as affected by variability of some physico-chemical properties.pdf
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Biol Fertil Soils (2011) 47:101
109
DOI 10.1007/s00374-010-0507-3
–
ORIGINAL PAPER
Field-scale variability of topsoil dehydrogenase
and cellulase activities as affected by variability
of some physico-chemical properties
Anna Piotrowska
&
Jacek D
ł
ugosz
&
Barbara Namys
ł
owska-Wilczy
ń
ska
&
Ryszard Zamorski
Received: 7 June 2010 /Revised: 27 September 2010 /Accepted: 28 September 2010 / Published online: 12 October 2010
#
The Author(s) 2010. This article is published with open access at Springerlink.com
Abstract We have studied spatial field-scale variability of
soil dehydrogenase (DH) and cellulase activities (CEL) and
their relationship with variability of some physico-chemical
properties at the surface horizon of the agricultural field.
Soil samples were collected at 50 points from the upper
20 cm of soil. The activity of DH ranged between 0.77 and
1.5
CEL activities in the north-east of the area, while the south
area showed the highest CEL activity. The DH activity
values were irregularly distributed in the surface horizon of
the studied soil and this behaviour did not correspond with
the spatial distribution of other properties.
Keywords Cellulase
.
Dehydrogenase
.
Physico-chemical
properties
.
Soil spatial variability
.
Geostatistics
M TPP·g
−
1
·h
−
1
while CEL activity ranged from 0.8 to
μ
M glucose·g
−
1
·24 h
−
1
. Concentrations of C
ORG
and
TN varied from 8.5 to 31.7 g·kg
−
1
and from 0.94 to
3.56 g·kg
−
1
, respectively. The soil data showed that spatial
variability and semivariograms describe spherical and linear
models with the nugget effect (DH, CEL, C
ORG
and TN).
Dehydrogenase activity was in the strong variability class,
while cellulase activity was situated in the week variability
class. Both C
ORG
and TN concentrations and pH
KCl
values
were strongly spatially dependent with the percentage of
total variance (sill) presents as nugget variance ranging from
8.9% to 16.1%. Kriged maps displayed the lowest values of
1.94
μ
Introduction
Soil spatial variability can be considered at different scales
such as microscale, plot scale, field scale, landscape and
regional scale (Parkin
1993
; Kandeler et al.
2001
; Lin et al.
2005
; Baldrian et al.
2010
). Spatial variability of soil
parameters at the field scale have both theoretical and
practical significance (Mulla and McBratney
2000
) and can
allow estimating real changes in soil properties for the
proper management of soil resources (Usowicz
1999
) and
the use of the
:
R. Zamorski
Department of Biochemistry,
Faculty of Agriculture and Biotechnology,
University of Technology and Life Sciences,
6 Bernardy
ń
ska St.,
85 029 Bydgoszcz, Poland
e-mail: ap03@wp.pl
A. Piotrowska (
*
)
“
”
. Soil variability of a
cultivated field is often neglected and physico-chemical and
biological parameters are measured by taking a single, quite
often composite sample. Rarely pot or micro-field experiments
reflect field variability (Usowicz et al.
2004
).
The main factors controlling spatial variability of soil
properties at the field scale are soil type, surface topography,
and water distribution. Soil type depends on properties as
texture, top soil thickness, organic matter content, pH and the
nutrient status (Parkin
1993
). A better understanding of the
nature of spatial variability of various soil properties, as well
as their relationship, should give real patterns of soil quality.
The investigations on some enzyme activities have been
usually carried out as pot or micro-field experiments under
more or less controlled conditions. There have been few
precision agriculture
J. D
ugosz
Department of Soil Science and Soil Protection,
University of Technology and Life Sciences,
6 Bernardy
ł
ska St.,
85-029 Bydgoszcz, Poland
ń
B. Namys
ska
Faculty of Civil Engineering, Institute of Geotechnics and
Hydroengineering, University of Technology,
Wybrze
ł
owska-Wilczy
ń
ż
e Wyspia
ń
skiego 27,
50-370 Wroc
ł
aw, Poland
102
Biol Fertil Soils (2011) 47:101
–
109
studies on the spatial variability of soil enzyme activities,
especially at field or regional scale (Bergstrom et al.
1998
;
Gaston et al.
2001
; Kandeler et al.
2001
;A
32
N
31
33
ş
kin and
49
30
50
Kizilkaya
2006
; Smoli
ski et al.
2008
; Gao et al.
2010
;
Baldrian et al.
2010
). Enzyme activities show a broad
spatial variability depending on the tested enzyme and on
soil environmental conditions (Kandeler et al.
2001
;Killham
and Staddon
2002
). Geostatistics has been used in soil
science to estimate spatial variation of physico-chemical
parameters (Usowicz et al.
2004
; Brodský et al.
2004
;Iqbal
et al.
2005
;Jungetal.
2006
) but it has been rarely used to
evaluate spatial-temporal fluctuations of biological parameters
(Parkin
1993
; Goovaerts
1998
;Morris
1999
; Saetre
1999
;
Mulla and McBratney
2000
).
Dehydrogenase activity (DH) plays a role in the
biological oxidation of soil organic matter and cellulase
activity (CEL) is important in soil C cycle (Makoi and
Ndakidemi
2008
) and thus it is important to study spatial
variability of these enzyme activities.
The aim of the investigation was to determine the spatial
distribution of dehydrogenase and cellulase activities in the
surface horizon of an acid soil and to compare them with
some physico-chemical properties of considerably differen-
tiated values. The acid soil was selected because it
represents the soils in Poland.
ń
12
34
29
11
35
13
28
46
10
36
14
27
45
47
9
37
15
44
26
8
48
16
38
43
25
7
17
39
24
6
42
18
40
23
5
41
19
22
4
20
3
21
2
1
Fig. 1 The soil sampling scheme
Soil analyses
Dehydrogenase activity (DH) was determined according to
Thalmann (
1968
) with some minor modifications. Soil (1 g)
was incubated for 24 h with 2, 3, 5-triphenyltetrazolium
chloride (TTC, 3 mgml
−
1
) at 27°C, pH 7.6. The produced
triphenylformazan (TPP) was extracted with acetone and
measured spectophotometrically at 546 nm. Dehydrogenase
activity was expressed as
Material and methods
The site description and the sampling procedure
MTPPg
−
1
d.m. soil·h
−
1
.
Cellulase activity (CEL) was assayed as reported by
Schinner and von Mersi (
1990
). Low molecular products
and sugars resulting from the enzyme degradation of
carboxymethylcellulose (Sigma Aldrich, 7 mgml
−
1
) for
24 h at 50°C and pH 5.5 were determined spectophoto-
metrically at 690 nm. Cellulase activity was expressed as
μ
μ
The studied field (50 ha) with varied relief (drop of about
20 m) was located at
popolska Plain, near the
Budniki Village, Warmia region, northern Poland (54
°
11
the S
ę
′
N, 20
°
38
47
E). Soil sampling scheme carried out in an
irregular grid pattern is shown on Fig.
1
. The soils of the
area are Eutric Cambisols, District Cambisols and Gleyic
Phaeozems (IUSS Working Group WRB
2007
). We
collected 50 soil samples in approximately regular intervals
(50 m) across the field. Each sample consisted of 30
individual sub-samples (30 g each) taken randomly from a
circle area with a radius of 10 m from the node point. The
only exception was some node points situated near the field
border where sub-samples were collected on the one side of
the node point. The samples were taken after harvest of
winter wheat (
Triticum aestivum
L.) and tillage and prior
seeding winter
″
′
35
″
M glucose·g
−
1
d.m. soil·24 h
−
1
. Control tests with auto-
claved soils were included in all enzyme assays to evaluate
the spontaneous or abiotic transformation of substrates.
Both enzyme activities are means of three replicates and are
expressed on a moisture-free basis. Moisture content was
determined by drying the soil samples at 105°C for 24 h.
Chemical analyses were performed on air dried and
sieved (<2 mm) soil samples according to standard methods
(Burt
2004
) and each sample was analyzed in triplicate.
According to the USDA (Soil Survey Staff
1999
), soil
samples were classified as loam (48% of samples), fine
sandy loam (26% of samples), clay loam (18% of samples),
sandy clay loam (6% of samples) and sandy loam (2% of
samples). Clay fraction content ranged from 8% to 39%. A
particle size distribution analysis was carried out by the
pipette method; the pH in 1 mol KCl
moist
samples were sieved (<2 mm) and stored at 4°C in a
plastic box for not less than 2 days to stabilize microbial
activity and then analyzed for dehydrogenase and cellulase
activities. Soil samples were analyzed for physical and
chemical properties after air-drying at room temperature
and sieving (<2 mm).
rape (
Brassica napus
L.). Field
–
dm
3
was measured by
⋅
Biol Fertil Soils (2011) 47:101
–
109
103
potentiometric method in 1:2.5 soil : solution suspensions;
total organic carbon (C
ORG
) and total nitrogen (TN) were
determined by the dry combustion CN analyser (Vario Max
CN).
Geostatistical analyses are based on original (input) databases
with values of coordinates X, Y and Z specifying sampling
(measurements, observations, etc.) and parameter (regionalized
variables) locations. The basic statistics of the investigated
parameters are roughly estimated and a structural
analysis of their variation, including the calculation of
isotropic and directional empirical variograms (covariograms),
is carried out. Then the (co-)variograms are modelled by
theoretical functions (the so-called geostatistical models)
and the adopted (co-)variogram models are cross-validated
using ordinary point kriging (Mulla and McBratney
2000
).
Statistical and geostatistical analyses
Data were evaluated with classical statistical methods
(STATISTICA v. 9.0) calculating arithmetic and geometric
means, standard deviation, coefficient of variation as well as
skewness and kurtosis. Geostatistical calculations included
empirical semivariograms graphs and theoretical mathematical
model of variograms. The following geostatistic parameters
were considered: nugget, sill variance, range of influence. To
classify the spatial dependence of soil properties we calculated
the percentage of total variance (sill) presents as random
variance Co
Results
Descriptive statistics of soil parameters under study
(Cambardella et al.
1994
). We
used the method of point kriging proposed by Davis (
1986
)
and the calculations were done using Isatis software
(Geovariance Co.). The maps illustrating the spatial variance
of determined parameters were drawn on the basis of
semivariograms.
A semivariogram is a measure traditionally defined as
half of the quadratic mean of the difference between two
values of a measurable parameters (the considered region-
alized variable), separated by the distance h (Burgess and
Webster
1980
). The semivariogram
½
ð
=
Co
þ
C
Þ
100
Basic statistical properties of the measurements within the
studied area are presented in Table
1
. Results of all
investigated parameters showed a normal distribution
according to Shapiro
Wilk test (Statistica v. 9.0), and for
this reason data were not transformed.
The DH activity of the soil surface ranged from 0.77 to
1.5
–
MTPP·g
−
1
·h
−
1
,
while cellulase activity ranged from 0.8 to 1.94
MTPP·g
−
1
·h
−
1
with mean value of
μ
μ
μ
M
glucose·g
−
1
·24 h
−
1
with mean of 1.06
Mglucose·g
−
1
·24 h
−
1
.
Most of DH activity was similar to the mean value, as shown
by the fact that median and the mean values were similar,
suggesting almost symmetric results distribution. Low kurtosis
value indicated that the DH activity distribution was similar to
the normal one.
Dispersion analysis of cellulase activity was characterized
by a very high concentration around the mean value, which
was confirmed by leptokurtic distribution (kurtosis 10.24),
underlining a slim distribution. Distribution of CEL activity
indicated that most of soil samples had enzyme activity lower
than the mean value, which was confirmed by a high skewness
value. The variation of coefficients obtained for DH was low
and for CEL was moderate according to the classes based on
coefficient of variation (CV; %) values proposed by Wilding
(
1985
) for assessing soil properties variability.
The C
ORG
and TN contents of the top soil ranged widely
from 8.5 to 31.7 and from 0.94 to 3.56 g·kg
−
1
,respectively
(Table
1
). A significant differentiation of the results was
indicated by the standard deviation value and variation
coefficient, showing that their differentiation was equal on
the studied area under. Additionally, the very high variance
value (14.46) showed a significant distribution of TOC
concentration. However, the dispersion analyses of C
ORG
concentrations revealed a high focusing around the mean
value, and this was confirmed by leptokurtic distribution
(kurtosis 11.05), while TN concentrations distribution was
similar to the normal one.
μ
γ
(h) was estimated
using the equation:
NðhÞ
1
2
NðhÞ
2
gðhÞ¼
ð
x
i
y
i
Þ
ð
1
Þ
i¼
1
where:
N
(
h
) is the number of sample pairs;
x
i
,
y
i
are the values
of the considered variable in a pair (respectively at
its
beginning and end).
Kriging is a technique of determining the mean value
used for
purposes, in which only the data
close to the tested area (search area
“
local estimation
”
kriging neighbourhood)
are used in the estimation (Mulla
1989
). The geostatistical
empirical variogram models were proposed for the area
where parameters were determined. The commonly used
kriging estimators are: ordinary kriging (when the
arithmetic mean is unknown) and simple kriging (when
the average estimated for the whole investigated sampling
population or for local estimations is known). The value of the
investigated parameter in the location
x
o
was estimated using
ordinary point kriging on the basis of
—
n
neighbouring
sampling points considering
x
α
a linear relationship with
weights
w
α
:
X
n
a¼
1
w
a
ZxðÞ
Z
»
xðÞ¼
ð
2
Þ
104
Biol Fertil Soils (2011) 47:101
–
109
Table 1 Statistics of soil
properties (
n
=50)
DH
CEL
C
ORG
TN
pH
KCl
Sand
Silt
Clay
Minimum
0.77
0.80
8.50
0.94
3.77
33.2
19.9
8.0
Maximum
1.50
1.94
31.70
3.56
6.70
69.1
45.4
39.0
Arithmetical mean
1.07
1.06
13.36
1.43
5.03
46.6
32.6
20.8
Geometrical mean
1.54
1.04
12.36
1.38
4.98
45.9
32.14
19.78
Variance
0.02
0.03
14.46
0.17
0.47
71.85
28.5
40.25
SD
standard deviation,
CV (%)
coefficient of variation,
DH
dehydrogenase activity
(
μ
M TPP·g
−
1
·h
−
1
),
CEL
cellulase activity (μM
glucose·g
−
1
SD
0.15
0.18
3.80
0.41
0.69
8.48
5.34
6.34
Median
1.09
1.01
12.40
1.33
5.09
44.55
33.35
21.0
CV (%)
14.2
17.4
28.47
28.93
13.68
18.19
16.37
30.5
·24 h
−
1
Kurtosis
0.67
10.24
11.05
1.27
−
0.18
−
0.18
−
0.11
0.26
), C
ORG
organic carbon (g·kg
−
1
), TN total
nitrogen content (g·kg
−
1
Skewness
0.36
2.45
2.77
3.10
0.13
0.56
−
0.25
0.32
)
ThepH(KCl)rangedfrom3.77to6.70withgeometrical
mean of 4.98. A significant dispersion of pH values was
confirmed by high GD, the variance values and the negative
kurtosis of
structure that could be best described by spherical models
for C
ORG
and TN contents, DH activity and clay percentage,
while spherical/linear models described CEL activity and soil
reaction. Spatial dependence of all parameters except clay
content showed short-range variability represented by nugget
effect. Parameters for these models are given in Table
2
and
Fig.
2a
0.18 (a flattened distribution). Similarly, the
negative kurtosis values were obtained for both sand and silt
contents confirming that the relative data was more flattened
than the normal one. As regards the granulometric fractions, the
highest differentiation was noted for clay, with a wide range of
values (8.0
−
f
.
The spatial dependence of the data was confirmed by sill
variance (Co+C), composed of structural (C) and nugget
variance (Co), except for the clay fraction content which
showed only structural variance. Almost all parameters
displayed a low nugget variance Co
–
39.0%), high SD and variance and by the high CV
value of 30.5%.
The analysis of correlation showed no significant
coefficient for most of investigated parameters (data not
presented). Only C
ORG
and TN were significantly and
positively correlated with the clay content (
r
=0.45
–
100
ranging from 8.9% to 21.2%. Nugget semivariances for
the CEL activity were very high as compared with sills and
nugget effects accounting for 94.7% of the total variance.
The spatial variability of the studied parameters was
categorized into classes based on the percentage of total
variance (sill) presents as random variance, proposed by
Cambardella et al. (
1994
). Almost all parameters indicated
a strong spatial variability (less than 25% spatial variability)
except for the CEL activity which had a poor spatial
variability (above 75% spatial variability). According to
Cambardella et al. (
1994
), the variability of weakly
spatially dependent parameters might be controlled by
factors, such as application of fertilizers and tillage, whereas
½
=
ð
Co
þ
C
Þ
0.47;
p
<
0.05;
n
=50) and significantly but negatively correlated with
the sand content (
r
=
–
0.52;
p
<0.05). No significant
correlation was found between pH
KCl
values and enzyme
activities, while pH
KCl
values were positively correlated with
the clay content (
r
=0.39;
p
<0.05;
n
=50) and negatively
correlated with the sand content (
r
=
−
–
0.50
−
0.32;
p
<0.05;
n
=50).
Spatial variability of studied properties
Generally, the soil data showed a spatial dependence
(Table
2
, Figs.
2
–
3
). All semivariograms exhibited a spatial
Table 2 Parameters of variogram models
DH
CEL
C
ORG
TN
pH
Clay
Model
Sph, NE
Sph, L
Sph, NE
Sph, NE
Sph, L
Sph
Co
0.0045
0.018
1.417
0.0167
0.046
–
(Co+C)
0.0212
0.019
15.917
0.184
0.139
35.44
½
Co
=
ð
Co
þ
C
Þ
100
21.2%
94.7%
8.9%
9.0%
16.1%
–
Range (m)
84.3
93.3
93.3
93.3
81.3
93.3
d
SD
S
W
S
S
S
–
Co
nugget variance,
C
structural variance,
Co+C
sill,
Sph
spherical model,
NE
nugget effect,
L
linear model,
SD
spatial dependence,
S
strong,
W
week,
DH
dehydrogenase activity (
M TPP·g
−
1
·h
−
1
),
CEL
cellulase activity (
μ
M glucose·g
−
1
·24 h
−
1
),
C
ORG
organic carbon content (g·kg
−
1
),
TN
total
μ
nitrogen content (g·kg
−
1
)
Biol Fertil Soils (2011) 47:101
–
109
105
Fig. 2 Experimental
semivariograms of:
a dehydrogenase activity
(
μ
M TPP·g
−
1
·h
−
1
),
b cellulase activity
(
μ
M glucose·g
−
1
·24 h
−
1
), c
C
ORG
content (g·kg
−
1
), d TN
content (g·kg
−
1
), e pH and f clay
percentage
a)
b)
c)
d)
e)
f)
strongly spatially dependent parameters are influenced by
variations in soil characteristics, such as texture and mineral-
ogy. The sampling scheme and statistical methods used in this
study however have not allowed to discriminating these two
sources of variability.
The ranges of the influence calculated for the microbial
parameters measured in this study ranged from 81.3 to
93.3 m. Since the samples separated by a distance smaller
than the range are related spatially (Flatman and Yfantis
1984
; Cambardella et al.
1994
), the range values of this
study showed that all variables were spatially autocorrelated
and either the sampling distance (50 m) was suitable or
samples might be even separated by the distance bigger than
50 m.
Semivariograms models of some the studied parameters
were used to generate kriged maps (Fig.
3a
d
). The spatial
trends in the CEL activity distribution were very clear. The
lowest activities were situated in the north-east of the area,
while the highest activities were in the south of area. A
band of a relatively average soil CEL activity run
diagonally across the field, from the south-east to the
north-west. The DH activities (Fig.
3b
) were irregularly
distributed in the surface soil and they did not correspond
with topographical features of the area or the mode of
spatial distribution of other properties. Kriged maps of
C
ORG
and TN contents (Fig.
3c, d
) showed approximately a
similar pattern distribution and their concentrations were
minimal in the north part of the area. Most of the highest
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