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Journal of Food, Agriculture & Environment Vol.11 (2): 1432-1436. 2013
www.world-food.net
Wind erosion rates for ten soils under threshold wind speed in controlled conditions
Rafal Wawer, Eugeniusz Nowocien and Boguslaw Podolski
The Institute of Soil Science and Plant Cultivation – State Research Institute, ul. Czartoryskich 8, 24-100 Puławy, Poland.
e-mail: [email protected]
Received 16 January 2013, accepted 30 April 2013.
Abstract
The Joint Research Centre of the European Commission’s data points at the soil erosion being the most frequent soil degradation process, estimating
its spatial extent to 17% of Europe, from which barely 8% is assigned to wind erosion. Although the processes of erosion are considerably well
recognized, their quantitative valuation which remains strongly variable between local conditions, still needs continuing and widening of research in
various spatial and temporal scales. The goal of the research, presented in this article, was to recognize qualitative and quantitative soil loss
mechanisms in result of deflation under the conditions of simulated wind at speeds around threshold value (8 m·s-1), performed for a representative
set of soil kinds for the geographical area of Poland. The deflation was performed and measured using an original, patented deflameter designed in the
Institute of Soil Science and Plant Cultivation. The duple regression model was investigated, resulting in well matched relationship between deflation
rate, soil humidity and the content of soil’s sand fraction. Investigated 10 soil kinds varied from the point of view of the response to wind blowing
with threshold speed of 8 m·s-1 for the time of 9 hours, generating a sediment volume ranging from minimum 2 for the strong silty loamy sand Fluvisol
to maximum 194 g m-2 for loose sandy Arenosol soil. The correlation analyses performed for the whole population of investigated soil kinds reveals
the most significant soil textural property in shaping the deflation ratio generated by a simulated wind remains the content of sand fraction and initial
soil humidity. The simple bifid regressions model, evaluated basing upon the measured values, explains 97% of observed variance and may be used
to estimate soil loss due to wind erosion in conditions of harrowed black follow during wind events with speed around threshold value of 8 m·s-1.
Key words: Wind erosion, threshold wind speed, deflameter, deflation.
Introduction
According to FAO about 1.6 billion hectares of plough land, (i.e.
about 13% of the area of continents) are at risk of erosive
degradation, over 1 billion hectares under water erosion, and about
550 million hectares under wind erosion 1. In Poland, the qualitative
erosion risk maps 2 estimate wind erosion to affect 28% of unforested land surface, while the area of land totally degraded with
soil erosion and unsuitable for agriculture is estimated to cover
700 thousand hectares.
Polish 2, 3 and international data 4-11 point at erosion as a main
soil degradation factor. Although the processes of erosion are
considerably well recognized, their quantitative valuation which
remains strongly variable between local conditions, still needs
continuing and widening of research in all spatial scales, starting
from plot throughout catchment till national and regional extents 6,
12
. Although investigations at a plot scale, being actually point
data, are considered unsuitable for country-wide erosion risk/
intensity assessments 6, 11 they are very valuable in testing and
validating modelling concepts 13-15. For instance the theoretical
equations within PESERA model have been calibrated using plot
measurements 6.
The are two main ways of field research regarding soil erosion:
the first, conducted in a passive way in natural conditions, without
intervention in course of erosion processes 6, 16, 17. The main
advantage of such approach is the reflection of real state whereas
the main disadvantage remains the long time period required for
1432
collecting sufficient amount of data for estimations of suitable
quantitative indicators. The second method 6, 18, 19: a simulated
research can be done in shorter time period, which accelerates the
estimation of interdependencies between factors and effects of
erosion processes and allows for better control of the value ranges.
Materials and Methods
Microplots for controlled research: In result of cartographical
studies, performed on 1:5000 digital soil maps, precise locations
of soil contours representing ten kinds of soil kinds were selected;
three species from each group differing with susceptibility to
deflation (Nowocien et al. 19) (Table 1): loose sands (pl), weak
clayey sands (ps) light clayey sands (pgl), strong clayey sands
(pgm), light loam (gl), medium loam (gs), ordinary silt (plz), loess
(ls), medium rendzina (Rs) and medium aluvial soil (Fs).
After confirming suitable granulometric composition in
laboratory investigations, in-situ samples were taken from every
appointed place for detailed measurements: granulometric
composition, structure, water properties (infiltration rate and field
capacity) and chemical analyses (organic matter, NPK and Mg).
The soil material from humus horizon (mostly subjected to erosion)
has been extracted as a base material for further model experiments.
The soil material was transported to experimental area and placed
to dedicated chests – microplots 18, 19 of 1 m x 2 m dimensions. The
plots were kept in permanent harrowed black fallow.
Journal of Food, Agriculture & Environment, Vol.11 (2), April 2013
Table 1. Textural parameters of chosen ten studied soils.
Fractional content %
(BN-78/9180-11)
Symbol
Sand
Silt 0.1- Clay <0.02
1-0.1 mm 0.02 mm
mm
Brunic Arenosol
loose sand
pl
Sand
90
5
5
Brunic Arenosol
weakly-loamy sand
ps
Sand
76
17
7
Haplic Cambisol
light loamy sand
pgl
Loamy sand
68
18
14
Cambic Albeluvisol
strong loamy sand
pgm
Loamy sand
60
20
20
Haplic Chernozem
light loam
gl
Sandy loam
52
22
26
Haplic Hernozem
medium loam
gs
Sandy clay loam
28
24
48
Haplic Cambisol
regular silt
pLz
Silt
13
67
20
Haplic Cambisol (Eutric) loamy silt (loess)
pLg (ls)
Silt loam
9
60
31
Rendzic Phaeozem
heavy loam
gc
Clay loam
29
6
65
Mollic Fluvisol
strong loamy silty sand pgmp
Sandy loam
45
36
19
Soil type
No.
(WRB 2006)
1
2
3
4
5
6
7
8
9
10
Texture
(BN-78/9180-11)
Texture
(USDA 22)
The research procedure for soils’ susceptibility to superficial
rinse off: The investigation of susceptibility of soil to deflation
was conducted during controlled wind blowing sessions using
an original mobile deflameter of Józefaciuk and Nowocien
construction (Fig. 1). Simulated deflation was carried out in a period
from early March to early October in favorable weather conditions
(positive temperature with absence of natural precipitation for at
least 5 days).
Each simulation was accompanied by measurements of initial
soil humidity, wind speed and amount of soil blown off and caught
by the cyclones. Simulations were ran in 9 hours long sessions.
The mechanism of deflation measurement in each micro-plot was
following 20, 21: soil material from a micro-plot was being deflated
by the simulated wind, which was generated with a regulated
radial blower and directed through a 0.5 m wide, 0.4 m high, 2 m
long wind tunnel placed tightly over a surface of a micro-plot. The
tunnel was tightly adjusted to the surface of a micro-plot to ensure
all the wind energy and soil mass stay within the tunnel. At the
time of the beginning of simulation the soil humidity at 4 levels: 5,
15, 25 and 35 cm was measured. The soil particles deflated and
transported by the simulated wind outside the micro-plot were
directed into two cyclones of the deflameter and deposited into
containers. After 9 hours of simulation the containers were
removed and the mass of eroded soil was measured.
Results
The simulated research on the soils’ susceptibility to deflation
was carried out in the years 2001-2011 during 20 sessions
performed for each of the 10 chosen soil kinds.
The amount of deflated material differed largely between
investigated soil kinds (Table 2). The highest deflation rate was
observed on Arenosol soil with the texture of loose sand (194
g·m-2), then on weak loamy sand Arenosol (43 g·m-2) and strong
loamy sand Albeluvisol (21 g·m-2). The smallest deflation was
observed on alluvial strong loamy silt sand Fluvisol (2 g·m-2).
The statistical analyses were performed on the population of 8
series of data (80 cases) on measured variables: initial soil humidity
at 5 cm depth [%] (Fig. 2) and deflation [g·m-2] (Fig. 3). Wind speed
and deflation time were constant in all the sessions, hence those
variables were excluded from analyses.
The analyses of the correlation matrices for independent
variables, soil humidity and contents of three fractions (sand, silt
and clay), and dependent variable deflation rate revealed the
highest significance of the content of sand fraction, which was
chosen as a parameter for development of a deflation model. The
correlations were investigated using linear, exponential and
polynomial fit functions.
The statistical analyses presented a basis for the development
of a soil loss function with soil humidity and content of the sand
fraction in soil. After investigating several linear and non-linear
models a bifid model was chosen, revealing an equation:
D = 19.73 · 0.241998 · Fp(%) – 211924 · W(%) + 39.11221 + 0.1 ·
Fp(%) + 55.32836 · W(%)
where D = deflation [g·h·m -2]; Fp = content of sand fraction in soil
[%] and W = initial soil humidity [%].
The fit quality (Fig. 4-6) and regression parameters of the
equation: r = 0.98, r 2 = 0.97, per cent of explained variance = 96.97%;
reflect relatively well representation of gathered results as
compared by the chosen model.
The equation should be interpreted as the relation between 1-
Table 2. Average values of observed deflation.
Plot No
Figure 1. The scheme of the model experiment of soils’ susceptibility to deflation
in conditions of simulated wind.
Journal of Food, Agriculture & Environment, Vol.11 (2), April 2013
1
2
3
4
5
6
7
8
9
10
Soil type Average initial Deflation
and texture soil humidity [%] [g·m-2]
Ar (pl)
7.1
194
Ar (ps)
7.4
43
Bk (pgl)
6.3
19
Ap (pgm)
12.1
21
Dz (gl)
14.5
4
Dz (gs)
15.1
8
Ap (páz)
21.1
14
B (ls)
10.4
19
Rc (gc)
4.8
9
Fs (pgmp)
6.5
2
1433
Normal P-Plot: WILG
Excepted normal at value
Number of observations
Excepted normal
70
60
50
40
30
20
10
0
0
5
10
15
20
25
X ⇐ Category boundary
30
35
3
2
1
0
-1
-2
-3
0
5
10
15
20
25
30
35
Value
WILG [%]
Summary statistics: WILG
Valid N = 181
Mean = 16.704530
Minimum = 4.600000
Maximum = 31.400000
Std. Dev. = 6.370303
32
30
28
26
24
22
20
18
16
14
12
10
8
6
4
2
Figure 2. Statistics for the variable of measured soil humidity WILG [%].
Normal P-Plot: DEFLACJA
Excepted normal at value
200
180
160
140
120
100
80
60
40
20
0
Number of observations
3
Excepted normal
-500
0
500 1000 1500 2000 2500 3000 3500 4000
X ⇐ Category boundary
2
1
0
-1
-2
-3
-500
0
500 1000 1500 2000 2500 3000 3500 4000
Value
1200
1000
Summary statistics: DEFLACJA
Valid N = 181
Mean = 123.437149
Minimum = 0.410000
Maximum = 3830.100000
Std. Dev. = 451.333950
800
DEFLACJA [g*m-2]
600
400
200
0
-200
-400
-600
-800
-1000
Figure 3. Statistics for the variable of measured deflation DEFLACJA [g*m-2].
Frequency distribution: Residuals
Excepted normal
Observed versus Predicted values
65
700
60
600
55
500
50
45
40
Number of observations
Observed value
800
400
300
200
100
0
-100
-100
30
25
20
15
10
0
100
200
300
400 500
Predicted value
Figure 4. Predicted versus observed values.
1434
35
600
700
800
900
5
0
-50
-40 -30 -20 -10
0
10 20
30
40
50
60
70
80
90
Figure 5. Distribution of equation’s rests.
Journal of Food, Agriculture & Environment, Vol.11 (2), April 2013
tunnel. Soil deflation rates caused by the wind speed of 8 m·s-1
were highly diverse among chosen soils, depending upon the soil
texture and moisture levels before the start of wind experiments.
The best correlation to deflation rates among textural classes were
observed for sand fraction. A bifid model was elaborated, binding
deflation rates with initial soil humidity and content of sand textural
class. The model slightly underestimates the deflation rates.
Future research will be focused on using differentiated wind
speeds for various soil texture groups, which should allow both
finding soil-specific wind threshold values and models as well as
evaluating a broader model for deflation considering also wind
speed and time.
Half-normal probab. Plot of residuals
3.5
3.0
Expected normal value
2.5
2.0
1.5
1.0
0.5
0.0
-0.5
-10
0
10
20
30
40
50
Abs (Residuals)
60
70
80
Figure 6. Half normal probability of equation’s residuals.
hour deflation in a given conditions of soil humidity for a given
soil caused by a wind of threshold speed of 8 m·s-1 blowing over
a fresh ploughed and harrowed land.
Acknowledgements
The research was funded within the statutory IUNG-PIB project:
“ Multiscale parameterization of simulation models within the
domain of hydrology, water and wind erosion, as tools for impact
assessments of agriculture onto the environment”.
References
Discussion
The results of the study should be regarded as representative
only to narrow number of cases in following conditions:
· Soil in black fallow, without any plant cover, fine harrowed to
small aggregates;
· Wind blowing with speed of 8m·s-1.
It represents rather narrow number of cases, occurring during
vegetation period, when fields are kept in black fallow, without a
plant cover, which takes place during and after the seeding or
after the post-harvest plough. Sample calculation of soil loss during
that period, based upon the rough results from simulations is
shown in Table 3.
Table 3. Soil loss in period of 14 days after seeding
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Mean Minimum
Maximum Standard
Soil
Texture kg·m -2 within 14 days after plough deviation
pl
76.57
23.50
268.41
80.00
ps
4.34
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7.02
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pgl
1.92
0.18
5.73
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pgm
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2.06
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5.11
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plg
1.36
0.02
3.70
1.31
gc
0.67
0.05
2.01
0.69
pgmp
0.35
0.08
0.64
0.15
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Conclusions
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from sand to clay loam (BN-78/9180-11) were performed in a wind
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