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International Journal of Agriculture, Forestry and Fisheries 2015; 3(3): 115-122
Published online May 30, 2015 (http://www.openscienceonline.com/journal/ijaff)
The Use of Fuzzy Logic and Geostatistical Techniques for Spatialization of Soil Texture in Part of Kano Close Settled Zone Kano State, Nigeria
Mohammed Ahmed
Faculty of Earth and Environmental Science, Department of Geography, Bayero University Kano, Kano State, Nigeria
Email address
To cite this article Mohammed Ahmed. The Use of Fuzzy Logic and Geostatistical Techniques for Spatialization of Soil Texture in Part of Kano Close Settled
Zone Kano State, Nigeria. International Journal of Agriculture, Forestry and Fisherie. Vol. 3, No. 3, 2015, pp. 115-122.
Abstract
Lack of spatial information on soil textural classes has attributed to the wrong usage of land for cultivation. The introduction of
Fuzzy logic and Geostatistic offers a convenient tool to help solving the problem of the use of generalization in data analysis
for soil texture using the conventional method. 51 soil samples from a gridded map of the area to the depth of 30 cm were
analysed in the laboratory and imported into Arc GIS 10.1. The purpose of this paper is to present a methodology that provides
the soil texture spatialization by using Fuzzy logic and Geostatistic in the study area. A Geostatistical interpolation algorithm
was employed to determine the prediction performances of the model. Semivariograms were produced for each soil textural
class. The results of the spatial distribution of the textural classes indicated that loamy sand has dominated the area. The study
shows that kriging was the best technique for each soil textural classes. The knowledge of the spatialization of soil properties,
such as the texture, can be an important tool for land use planning.
Keywords
Soil, Kriging, Co-Kriging, GIS, Semivariogram, Geostatistics, Fuzzy Logic
1. Introduction
Soil textural class is one of the important components that
determine the soil nutrient and the available water holding
capacity which has several implications for management in
agricultural. Distribution of soil texture in a cultivated land
can help in identifying areas suitable or unsuitable for a
particular crop. Furthermore understanding the rate of
variation of soil texture in the area has been identified by
Essiet (1995) which shows that sand fraction exceed 70 %
but except in hydromorphic soils that is found within the
river valley with less sandy proportion.
Analyzing the distribution of soil texture has been carried
out using conventional approach which most of the result
were presented in tables, that is to say sandy, loamy, clayey
soil etc, (in percentages). This type of analysis affects the
interpretation of the soil texture in the area because of the use
of mean or average. Value of a particular sample (point) may
go higher than the rest of the values (outlier) which may
probably be as the result of error analysis in the laboratory.
Conventional soil sampling procedures (using soil units as a
criteria) affects the result which causes error in the result
obtained, the values obtained are good, but the interpreter
may realize that many of the values in the field are (outliers)
either less than or greater than the values determined (Mahler
and Tindall, 1990).
Another point is that, soil parameters are continuous data,
therefore care need to be taking when classifying soil
information by delineating boundary as soil units. Yes, soil
may have boundary but that depend on the soil management
practices in the area or other factors of land characteristics
(like geology, lower and upper terrace, farming system or
management etc). The boundary of many geographical
features is an artifact of human perception, not of the real
world (Fisher, 1999). However, the result of this type of
analysis may yield an error because of the uncertainty and
vague this has been stated in Mahler and Tindall (1990). The
knowledge about the uncertainty is crucial for proper
understanding of the content of the map; therefore, if an
agreement on how to express the map quality can be reached,
then the information should be documented and released to
users according to Longley et al., (1999) in Kˇremenov´a
(2004).
International Journal of Agriculture, Forestry and Fisheries 2015; 3(3): 115-122 116
This study attempted to solve the problem in analyzing and
interpretation of soil texture using mapping technique. The
spatial distribution of geographical phenomenon (like soil
texture) could be mapped and analyzing the distribution and
concentration of a particular textural class. Kˇremenov´a
(2004) lamented that many spatial phenomena are inherently
fuzzy or vague or possess indeterminate boundaries. Fuzzy
logic has been applied for many areas in GIS such as fuzzy
spatial analysis, fuzzy reasoning, and the representation of
fuzzy boundaries.
Fuzzy logic is a mathematical model that is used in
analyzing sets whose elements have degrees of membership.
Fuzzy sets were introduced by Zadeh (1965) as an extension
of the classical notion of set. It is a form of many-valued
logic which deals with reasoning that is approximate rather
than fixed and exact. Compared to traditional binary sets
(where variables may take on true or false values), fuzzy
logic variables may have a truth value that ranges in degree
between 0 and 1. It has been extended to handle the concept
of partial truth, where the truth value may range between
completely true and completely false (Zadeh,1965).
A basic application might characterize sub-ranges of a
continuous variable. For instance, a fertility status of soil
could be determine using one of the factors like soil organic
matter, the measurement may have several separate
membership functions defining the ranges needed to show
the status of the soil. In this case, the analysis could be
presented in either fertile or not fertile which is 1 or 0, but the
problem with this is the possibility of having such kind of
result without defining a boundary or a criteria which will
help in determining the differences in classes of fertility like,
slightly fertile, moderately fertile, highly fertile and so on.
This could be presented using values that ranges from 0 to 1,
pending on the values were categorized in the analysis
(Ahmed et al, 2015).
Fuzzy set theory has been widely used in soil science for
soil classification and mapping, land evaluation and
suitability, soil geostatistics, soil quality indices and decision-
making analyses (McBratney et al., 2003; Lagacherie, 1997;
Busscher et al., 2007; Torbert et al., 2000; Keshavarzi et al.,
2011; Ahmed et al., 2015). While Kˇremenov´a (2004)
applied a fuzzy model to develop a soil map using the
knowledge of geologists, the study shows that fuzzy expert
system, capable to handle the spatial variability of soil
behavior. In these studies no attempt were made to classify
and analyse the distribution of soil texture.
2. Study Area
Bunkure LGA is located between Latitude 110 34’ 02’’N to
Latitude 110 46’ 05’’N of the Equator and between Longitude
80 26’ 36’’E to Longitude 8
0 46’ 43’’E of the Prime Meridian.
The study area is comprises of fifteen wards (15) with an
aerial extend 9911.22 Km2 and is bordered with Dawakin
kudu and Kura LGAs by the North, Wudil and Garko LGA
by the East while Kibiya at the South Western part of the
study area (Ahmed et al, 2015).
The area experiences four distinct seasons, long wet
(damina) and dry (rani) seasons and short autumn (kaka) and
spring (Bazara) seasons closely associated with the
movement of the ITD. The mean annual Rainfall is about
884mm varying greatly from the northern and southern parts
of the Region (Maryam, Halima and Ummi, (2014) and
Usman, (2014)).
Figure 1. Study Area.
117 Mohammed Ahmed: The Use of Fuzzy Logic and Geostatistical Techniques for Spatialization of Soil Texture in Part of Kano Close
Settled Zone Kano State, Nigeria
Figure 2. Sampling Points.
Figure 3. Converting of the textural classes of soil using fuzzy logic (Adopted from Camarinha et al, 2011).
The geology of Bunkure has been dominated with carse
porhyntiuc biotite and Biotite hornblende Granite which is
under the Pan African Older Granite series within the
Pericambrian to Cambrian Basement Complex (NGSA,
2006). Latosols are the dominant soils in the wind drift of
Kano state. They are well-drained and brownish to reddish in
colour. They are also deep except where iron pans are
exposed or occur near the surface (Olofin, 1985 and Ahmed,
2006). About 75% of the land is cultivated parkland with
average tree densities of less than 25 per hectare (Badamasi,
2013).
International Journal of Agriculture, Forestry and Fisheries 2015; 3(3): 115-122 118
3. Materials and Methods
Soil samples were taken from the depth of 0 – 30 cm using
a grid of 1500 x 1500 meters (Figure 2). In order to facilitate
the field work and to ensure the accuracy of the
examination/sampling points, it were uploaded on Global
Positioning Systems (GPS) Garmin 76csx model and the soil
examined and sampled according to MARDITECH (2011),
Wollenhaupt et al., (1994); Rehm et al., (2001) and Umar
(2011).
The samples were taken to the Standard laboratory (Soil
and Water Lab. of the Department of Geography Bayero
University Kano) air dried and gently crushed with porcelain
pestle and mortar; and then passed through a 2mm sieve to
remove coarse fragments. The fine earth samples (<2mm soil
portion) collected were analyzed. Particle size distribution
was determined using hydrometer method (Gee and Bauder,
1986). Sand, silt and clay were determined by dispersing the
soil samples in 5% calgon (sodium hexametaphosphate)
solution. The dispersed samples were shaken on a
reciprocating shaker after which particle size distribution was
determined with the aid of Bouyoucous hydrometer at
progressive time intervals. The textural classes were
determined with the aid of USDA textural triangle.
The next step is to arrange the textural classes based on the
percentage of clay content (using rating) which could be
converted in numerical values (fuzzy values), using the fuzzy
logic concept (see figure3). Thus, two extremes were
established, representing predominantly sandy soils (values
near from 0) and clayey soils (values near from 1). All
samples were classified numerically according to their degree
of relevance on the texture (Camarinha et al, 2011). The
results were entered into the Microsoft Excel with their
respective coordinates (Latitudes and Longitudes) and
transformed into GIS environment using Arc GIS 10.1
(version) in order to analyse the spatial distribution of the soil
texture in the area according to Mustafa et al, (2011).
Figure 4. Distribution of soil texture.
Table 1. Fuzzy values representing the soil texture for all samples.
Sample Fuzzy value Sample Fuzzy value Sample Fuzzy value Sample Fuzzy value
1 0.04 26 0.16 13 0.12 38 0.36
2 0.04 27 0.2 14 0.12 39 0.4
3 0.08 28 0.24 15 0.12 40 0.4
4 0.08 29 0.24 16 0.12 41 0.44
5 0.12 30 0.28 17 0.12 42 0.44
6 0.12 31 0.28 18 0.12 43 0.45
7 0.12 32 0.32 19 0.12 44 0.5
8 0.12 33 0.36 20 0.16 45 0.5
9 0.12 34 0.36 21 0.16 46 0.5
10 0.12 35 0.36 22 0.16 47 0.54
11 0.12 36 0.36 23 0.16 48 0.6
12 0.12 37 0.36 24 0.16 49 0.7
25 0.16 50 0.8
51 0.9
Sources: Data analysis (2014)
119 Mohammed Ahmed: The Use of Fuzzy Logic and Geostatistical Techniques for Spatialization of Soil Texture in Part of Kano Close
Settled Zone Kano State, Nigeria
Seminariogram models were tested from Arc GIS 10.1. In
predicting spatial process at nonsampling sites using
geostatistics, it is necessary to decide on a theoretical
variogram based on the experimental variogram. It is vital to
choose an appropriate model to estimate spatial statistics as
each model yields different values for nugget, sill, partial sill,
spatial dependency and range which are essential for
geostatistical analyses (Trangmar and Uehara, 1985).
4. Results and Discussions
By the methodology described previously, all samples
have a numerical value to represent its textural class (fuzzy
value), as presented in the Table 1. Geostatistical extension in
the software was used for spatial interpolation. This is a
value of a variable at unsampled locations. The interpolation
techniques commonly used in earth sciences include Semi-
variogram of Ordinary Kriging which was used to produce
the spatial distribution of the soil texture. The equation of
semivariogram can be defined as stated by Webster and
Oliver (1993):
ý(h) = 1/2N(h)∑ �Z�x��– Z�x� h�� ����
���
Where ( ) i Z x is the value of the regionalized variable Z(x)
at location of i x, Z(x h) i + is the value of the variable Z(x) at
location of (x h) i + , and N(h) is the number of pairs of
sampling sites separated by the lag distance h
The next step was to analyze the spatial characteristics of
the variable (texture) in order to verify the viability of the
spatial interpolation of data by kriging. To apply this
geostatistical method, it is necessary that the spatial
distribution of the variable (texture) meets certain
geostatistical assumptions. Geostatistics uses the technique of
variogram to measure the spatial variability of a regionalized
variable, and provides the input parameters for the spatial
interpolation of kriging (Webster and Oliver, 1993).
From the verification and settings of the semivariogram
presented in Figure 4, the Geostatistical Wizard tool is able to
generate the spatial distribution map of the texture, by
interpolation of the spatial data. The result shows the
variability of the texture in whole study area. The
concentration of more soil with clay content while at the
southern part of the area showing moderate concentration and
the extreme Northern site is more of soil with sandy content.
4.1. Spatial Structure of Soil Texture
The best fit model was selected using semivariogram
models to determine the textural variability in the area. From
different models tested it indicated that “Exponential” was
the best fitted with the Root Mean Square Error (RMSE) of
the least value of 0.2027. While the lag size (m), range (m),
Nugget, partial sill and sill of 388.5, 2,798.17m, 0.02550,
0.0173 and 0.0428 respectively (Table 2 and figure 5)
Table 2. Values of different model fittings of semivariogram of soil texture.
Variable Depth
(cm)
Lag size
(m) Model
Nugget
(Co) Sill (Co+C)
Spatial Dependency
Co/(Co+C) (%)
Partial sill
(C)
Range
(m) RMSE
Soil texture 30 388.5 Exponential 0.0255 0.0428 0.596 0.0173 2,798.17 0.2027
Sources: Data analysis (2015)
Data analysis (2015)
Figure 5. Semivariogram model of soil texture.
International Journal of Agriculture, Forestry and Fisheries 2015; 3(3): 115-122 120
Figure 6. Fuzzy classification for textural classes.
The semivariogrm obtained from the experimental data
often had a positive value of the intersection with the
variogram axis expressed by the named nugget effect Co.
This can be explained by sampling errors, shortage variability,
and unexplained and inherent variability. It can also indicate
the irresolvable variance that characterizes the micro-
inhomogeneity at the sampling location (Jayeoba et al, 2013).
Some semivariograms are generally well structured with
small nugget effect. It showed that the sampling is adequate
to reveal the spatial structures (McGrath et al, 2004).
The nugget/sill ratio or Spatial dependency Co/(Co+C)
defines the spatial property. The variable is considered as a
strong spatial dependence when the value of Co/(Co+C) is
less than 0.25, a moderate spatial dependence when this
value is between 0.25 and 0.75, and a weak spatial
dependence when the value is more than 0.75 (Cambardella
et al, 1994). The Spatial dependency Co/(Co+C) for the soil
texture at the depth of 30cm, these shows a moderate spatial
dependency with value of 0.596 (within 0.25 to 0.75). This
revealed that the spatial distribution of soil texture at the
30cm depth in the area is dominated by human activities such
as irrigation can cause some variation among the textural
classes. These suggested that the extrinsic factors such as for
plowing and other soil management practices weakened their
spatial correlation after a long history of cultivation.
4.2. Fuzzy Classification for Textural Classes
The fuzzy analysis shows that the soil of the area has been
dominated by mostly sandy particles. Three soil textures
were identified in the area which includes; loamy sand, sandy
loam and sandy clay loam. The soil textural classes has been
affected by it Geological formation and soil type. The soils of
the area according to Esseit (2013) formed over the
Basement Complex rocks which are relatively well structured
and posses sufficient depth to permit the cultivation of most
staple crops. The Basement Complex rocks are quite variable
in size and composition and include schists, shales and
granites among others.
Loamy sand in Figure 5 occupied 72.53% of the land in
the area followed by sandy loam with 17.82% while sandy
clay loam with 9.65%. This shows that places around the
Southern part of the area realized some patchy concentration
of sandy loam soils and also at the central area towards the
Eastern part experienced the presence of sandy loam and
sandy clay loam within an area which indicated a more clay
in the area than other places, this is as the result of closeness
to the river in the area which is more of hydromorphic type
of soils (Essiet 1995). The dominance of loamy sand in the
area has been characterized by Essiet (2013) as ferruginous
type and that is also associated with Basement Complex in
the areas.
5. Conclusion and Recommendations
The results of the spatial distribution of the textural classes
indicated that loamy sand has dominated the area. The study
shows that kriging technique is one of the best techniques for
classifying soil textural classes. This approach can improve
121 Mohammed Ahmed: The Use of Fuzzy Logic and Geostatistical Techniques for Spatialization of Soil Texture in Part of Kano Close
Settled Zone Kano State, Nigeria
on the use of conventional method by saving time, manpower
and energy, also combining kriging and fuzzy logic provide
better data management which helps in modeling. The
knowledge of the spatialization of soil properties, such as the
texture, can be an important tool for land use planning and
agricultural sustainability.
Therefore, the study recommends for proper soil
investigation particularly at the larger scale using GIS and
Remote sensing techniques.
Acknowledgements
This is to acknowledge the contributions of Mallam
Murtala U. Mohammed, Mallam Abubakar of the Soil and
Water laboratory and Dr. Adnan Abdulhamid of the
Department of Geography Bayero University Kano, for their
assistance and guidance especially during data collection,
analysis and presentation.
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