Journal of
AGROBIOLOGY
J Agrobiol 29(2): 47–54, 2012
DOI 10.2478/v10146-012-0008-0
ISSN 1803-4403 (printed)
ISSN 1804-2686 (on-line)
http://joa.zf.jcu.cz; http://versita.com/science/agriculture/joa
ORIGINAL ARTICLE
Assessment of different methods of soil suitability classification
for wheat cultivation
Amin Sharififar
Faculty of Agriculture, Shahrood University of Technology, Shahrood, Iran
Received: 24th November 2012
Revised: 1st April 2013
Published online: 31st October 2013
Abstract
This study investigated the impact of soil temperature and soil moisture on the virulence To
protect soil resources and also to achieve optimal crop production, it is essential to dedicate
the most suitable land to a specific land use. Achieving this goal is possible through land
use planning in conjunction with land evaluation. In this study a land suitability evaluation
was carried out for wheat (Triticum aestivum) cultivation, and was performed in the Bastam
region located in the north east of Iran. 104 soil profiles were sampled and 11 land units were
separated. In order to find out the most correct method of physical land suitability evaluation,
three methods of combining soil criteria for soil index calculation for wheat production were
tested. These methods are based on parametric and maximum limitation approaches, and
the results of each method were compared with the observed yield. Ultimately, the maximum
limitation method was found to be the best method and was used for classification of the
suitability of the study area lands for wheat cultivation. The varying results of applying
different ways of evaluation in this study indicate that the accuracy of the method of land
evaluation adopted should be checked before using the results for any purposes.
Key words: Almagra model; Bastam; land evaluation; MicroLEIS; soil index
INTRODUCTION
vital for wise planning of land use. Before making
any decisions about dedicating lands for any
agricultural uses, land suitability evaluations
should be implemented. Technically each land
unit should be used for an application which is
suitable for that application (FAO 1978). Suitable
land use planning paves the way for sustainable
development.
Land evaluation makes it possible to use lands
according to their biophysical potentialities and
limitations, in order to protect soil resources
from degradation and at the same time to meet
farmers’ demands for optimal crop production.
Scientific recognition of land resources and
possible land exploitations, as well as interactions
between specific land units with a specific use, is
Amin Sharififar, Faculty of Agriculture,
Shahrood University of Technology, Shahrood,
36199-95161, Iran
[email protected]
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If there is a significant correlation between
predicted yield and observed yield, then the
results of the evaluation method would be
accurate and can be applied for land suitability
classification and management. Two general
methods of land evaluation have been presented
by Sys et al. (1991b). Those methods are the
maximum limitation method and parametric
method. The so-called Almagra model (found in
the MicroLEIS software) which is based on the
maximum limitation method for land suitability
assessment, is applied in this study.
MicroLEIS DSS (Decision Support System)
software, containing the Almagra model was built
in the Mediterranean climate (De la Rosa et al.
2004) and has been recalibrated and revalidated
in semi-arid regions in west Asia (Shahbazi et al.
2009). Some case studies have used the Almagra
model for land suitability evaluation (Darwish et
al. 2006, Wahba et al. 2007, Shahbazi et al. 2008,
2009, Jafarzadeh et al. 2009) but unfortunately
researchers in such case studies have not
investigated their method in comparison with
other existing methods to find out whether the
method that was used is the correct one. Models
like Almagra need to be validated when they are
used in areas other than those for which the model
was calibrated and validated. The evaluation
methods discussed in this paper have been used in
other regions but most of those researchers have
tested only one method of evaluation and did not
compare the results of different methods for land
evaluation. Some of them have not investigated
the correlation of predicted results with the real
observed yield of the crop. The objective of this
study is to select the most correct land evaluation
method and then determine the suitability of
land units for wheat cultivation.
varied from 1350 to 1900 m above sea level but
elevated lands are not cultivated and the altitude
of agricultural lands does not vary significantly.
The total area surface was about 53,500 ha. Slope
gradient varied from flat to 8%. The physiography
of the studied land units included: Gravelly
Alluvio-Colluvial Fans, Pidmont Plateax and
Alluvial Plains. According to the bioclimatic map
of the region (FAO 1988), the study area possesses
an attenuated sub-desert climate. Major land
uses of the study area are agricultural, pasture
and fallow lands. As wheat is a strategic crop in
many countries including the study area and also
most of the farmers dedicate a high surface area
of the region studied to wheat cultivation each
year, this crop was selected for evaluation to be
tested for soil suitability evaluation.
Soil sampling and analysis
In total, 104 soil profiles were investigated and
among those, eleven representative profiles
were selected. Therefore 11 representative land
mapping units, taxonomically classified to the
family level, were separated. The procedure of
taxonomic land classification was adopted from
the soil taxonomy manual of the United States
Department of Agriculture (USDA 2010). This
classification is based on field surveys and
morphological descriptions such as leaching
evidence, the position of soil horizons and their
depth, and chemical and physical analyses
such as: electrical conductivity, organic carbon,
exchangeable
sodium
percentage,
cation
exchange capacity, carbonate content, texture,
structure, etc. These analyses were carried out
using standard methods of soil analysis in the
laboratory. Land unit separation was carried
out by field investigations. Some climatic
data, including temperature and precipitation
rates were also used in the taxonomic land
classification, which also showed Aridisols
and Entisols as dominant soil classes in the
region. Soil moisture regimes were Aridic and
Torric, and the thermal regime was Mesic. The
geographical position of the land mapping units
(soil families) of the region is shown in Fig. 1
and the measured site characteristics are shown
in Table 1.
MATERIALS AND METHODS
Study area description
This study was performed in the Bastam region
in the Semnan province located in the north east
of Iran. The study sites were located between
coordinates 54° 39′ to 55° 20′ of east longitude
and 36° 26′ to 36° 45′ of north latitude. Altitude
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Table 1. Mean values of land characteristics of the study area
Land units
Texture1
class
Electrical
conductivity
(ds/m)
Carbonate
content %
ESP2
Rooting
depth (cm)
pH
Drainage
class
Stoniness
%
15–35
Abr
Sandy Loam
0.4
31
2.7
95
7.8
Good
Amir
Clay Loam
1.7
23.7
3.1
145
7.9
Moderate
<15
Bagh
Loamy Sand
1.0
5.5
2.8
22
7.9
Good
30–75
Bastamy
Loam
1.7
28.5
6.3
160
7.8
Moderate
<15
Bayazid
Loamy Sand
1
14.7
4.1
95
8.0
Good
15–35
Kharaqan
Silty Clay
6.8
55.8
13.2
125
7.8
Moderate
<15
Khazaneh
Sandy Loam
0.5
24.0
2
40
7.9
Good
15–35
Khij
Sandy Loam
1.7
36.0
1
86
7.8
Good
10–25
Mojen
Silty Loam
0.8
41.5
2.6
150
8.0
Moderate
<15
Qaleh
Silty Clay
Loam
2.7
30.2
5.8
150
8.0
Moderate
<15
Qehej
Sandy Loam
0.7
20
1
140
8
Good
46
1
Texture classes as well as all other parameters values are mean values of the soil profile horizons, according to the instructions
in Sys et al. (1993)
2
Exchangable Sodium Percentage of soil
given to each criterion. The total score for a
special land unit is also given a rate of 0 to 100 by
calculation through the three methods discussed
in this paper as ways of combining the criteria
scores. The procedure of the maximum limitation
method is the the selection of the most restricting
criterion rate and considering it as the total score
for a land unit.
The wheat yield data in each land unit were
used as indicators of soil potentiality which in
turn indicates soil productivity. However, the
yield is not dependent only on soil and land
characteristics but could also be influenced by
managerial factors and other factors such as
diseases, which have not been considered in the
land evaluation. However, after interviews with
the farmers of the region, managerial differences
among land units of the region were seen as
unimportant, and therefore the soil productivity
potentiality level can be distinguished using crop
yield.
Evaluation procedure
In this study three ways of determining the total
ranking of every specific land unit are applied
and their outcomes are compared with soil
productivity. The three methods, explained by Sys
et al (1991a), are the Storie method, the square
root method (Khiddir 1986) and the maximum
limitation method. The Storie method and the
square root method can both be subsumed under
the rubric of parametric methods. The equation
(1) and (2) show the Storie and square root
methods respectively.
SI = (A) × (B/100) × (C/100) × …
(1)
SI: Storie index
A, B, C: ratings of criteria
I = (Rmin) × √A/100 × √B/100 ×…
(2)
I: index of square root method
Rmin: the minimum rated criterion
A, B, … : criteria other than minimum rated
criterion
Crop potential yield
To compare the predicted soil suitability
(potentiality) for wheat growth with the actual
yield, the score (called the soil index) of each
land unit, having been calculated using three
methods, is multiplied by the crop potential yield,
and the outcome is compared with the observed
yield in each land unit. In this study the FAOAEZ method (Food and Agriculture Organization
The rate for each criterion is obtained
after field or laboratory measurements of the
land properties, and the comparison of these
measurements with the crop requirements in the
reference tables. After matching measurements
with threshold values, a rating of 0 to 100 is
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Agro-ecological Zoning project) (FAO 1996) was
used to calculate the wheat potential yield. In
the AEZ model, biomass production and the crop
yield is calculated by using sunlight radiation
and temperature in an ideal situation in terms
of water and nutritional requirements, and
diseases. The following formula is the formula for
calculating crop yield:
Y = 0.36 bgm.KLAI.Hi / [(1/L) + 0.25Ct]
Where, bgm is maximum gross biomass
production (kg CH2O ha.hr), KLAI is maximum
growth ratio, Hi is harvest index, L is growth
period (number of days), Ct is respiration
coefficient and Y is crop potential yield (kg ha).
The potential yield is not affected by soil
characteristics and cultivation management. A
detailed explanation of the calculation of crop
potential yield can be found in Sys et al. (1991a)
and Ayoubi and Jalalian (2010).
(3)
Fig. 1. Soils taxonomic classification of the study area
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shown in Table 3. The potential yield for wheat
was calculated as 6709.4 kg ha, using the AEZ
method. The correctness of this method for
calculating potential yield has been tested in
different parts of the world through the FAO agro
ecological zoning projects.
In the country of the study area, several studies
have also shown the validity of the AEZ method
for calculating potential yield: Ayoubi et al. (2002)
determined the potential yield of wheat, barley
and corn in Isfahan in the centre of the country
as 9.08, 9.7 and 12 ton ha respectively; Bazgir et
al. (2000) also determined the potential yield of
wheat and barley as 7634 kg ha and 7487 kg ha
respectively in Kermanshah in the west of the
country. The AEZ method for calculation of the
potential yield of wheat, corn and sesame was also
applied by Rostaminia (2001) who obtained 7.42,
9.22 and 1.44 ton ha for those crops respectively
in Ilam which is in the southwest of the country
of the study area.
The correlation of the predicted potentiality
of wheat cultivation, calculated through the
three methods, with real wheat yield in different
land units is shown in Fig. 2. The yield values
(Table 3) are the average of harvests over several
years for the region registered in the Agriculture
Organization of Bastam and are used as basic
data.
Almagra model
The Almagra model was applied to determine
the suitability classes as a method of maximum
limitation. The Almagra computer-based model,
makes the evaluation task easier and more
precise, since human error is reduced using
this method. Complete information about the
MicroLEIS package can be found in De la Rosa
et al. (1992, 2004). The parameters considered as
data inputs for the Almagra model are shown in
Table 2.
Table 2. Input parameters considered for Almagra
model
Rooting depth
Texture class
Drainage class
Carbonate content
Salinity
Degree of development of the soil profile
RESULTS AND DISCUSSION
The results of total land ranking for every
land unit by using the Storie, square root and
maximum limitation (the Almagra model) are
Fig. 2. Results of correlations between the three
methods of land evaluation and observed yield of
wheat
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Table 3. Results of land units ranking by Almagra model, square root method and Storie method compared
with observed yield
Land units
Almagra
Square root
Storie
Mean observed yield of wheat (kg ha)
Abr
S41t2 (30)3
21
15.09
2200
S2t (70)
54.7
42.79
3200
S2ta (68.75)
46
30.79
4000
Amir
Bastamy
Bayazid
S5t (15)
15
15
–
S3cs (57.5)
28.9
14.62
2800
Khazaneh
S4t (30)
28.7
27.6
–
Khij
S4t (30)
24
19.5
2100
Mojen
S2t (66.66)
52
40.74
3000
Qaleh
S2ca (78.3)
64
52.97
3400
Qehej
S4t (30)
29
28.5
2000
Kharaqan
1
S, suitability class; S1, optimum, S2, high, S3, moderate, S4, marginal
2
Soil limitation factors; a, sodium saturation; c, carbonate content; s, salinity; t, texture
3
Values in the parenthesis are the quantified rankings
Storie soil index the and observed yield (R2 = 0.72)
for oil palm was found by Embrechts et al. (1988).
Different soil indices found by other researchers
means that the Storie equation gives different
results when the scores for the characteristics
vary. The maximum limitation method for land
suitability evaluation has been used by other
researchers such as Biox and Zinck (2008) as a
sound method. They determined the suitability
of their study area lands for alfalfa, maize,
wheat and some other crops. Examples of land
suitability studies in agriculture can be found
in the literature (Kalogirou 2002, Ceballos-Silva
and Lopez-Blanco 2003a, b).
The higher correlation of the Almagra
model with the observed yield is maybe due to
consideration of the most limiting characteristic
which controls crop production, because in
the maximum limitation method there is
an assumption that it is the most limiting
characteristic that determines the productivity
of a land. The maximum limitation method is
used when we are not sure about the quality
and quantity of complex interactions among
land characteristics and how to combine those
characteristics.
Therefore,
the
maximum
limitation method gives a more objective result in
comparison with other methods that combine the
land characteristics scores.
The method used to combine the land criteria
to come to a final total score for a specific land
unit in the land evaluation process is important
The maximum limitation method shows the
best correlation with soil actual potentiality. To
find the significance of correlations, the t-Student
test was performed. The answers obtained by the
test were compared with the threshold values
at α=0.05 and α=0.1 (certainty level of 95% and
90% respectively). The results showed that the
correlation between the yields predicted using the
maximum limitation method, and the observed
wheat yields was significant at both levels of
certainty. But the correlation between both the
square root and the Storie methods with the
observed yield were not significant statistically
at the two levels of certainty. Therefore, the
results of the Almagra model were used for the
classification of the suitability of the region for
wheat cultivation. The results of this type of
evaluation can be used for future remediation of
soils, since the restricting factor is distinguished.
The best site for wheat production among all the
arable sites was the Bastam site and the worst
was the site Bayazid site (Table 4). The most
limiting factor in the Khazaneh, Abr and Bayazid
sites was texture and in the Kharaqan site it was
also soil texture as well as high carbonate content
and salinity.
The Almagra model presents qualitative
suitability classes for crops, but the qualitative
classes were quantified by referring to the software
database. This is needed for a quantitative
comparison between Almagra and the other two
methods. A significant correlation between the
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because different ways of calculating total land
index result in different outcomes, as has been
shown in this research. To make a correct and
precise decision on land management, a precise
and sound method of land evaluation should be
adopted, and a precise and sound method can be
obtained through testing the correctness of the
method.
Two general factors including soil attributes
and climatic situation, affect crop growth. In
the Almagra model, climatic parameters were
not considered, as we assumed that climate
variability among land units is negligible in
the study area. In this study, a soil-specific
suitability classification of separated land units
was carried out for wheat cultivation. Usually,
increasing agricultural land capability correlates
with a decrease in soil erosion processes, and a
positive correlation between current land use and
potential land capability would be necessary (De
la Rosa and Van Diepen 2002).
Table 4. Current land uses and surface area of different land units
Land units
Surface area (ha)
Surface %
Current land use
Slope %
Abr
8125
15.19%
Wheat and fallow lands
0–5
Amir
1525
2.85%
Agricultural lands and pasture
0–2
Bagh
7250
13.56%
Pasture
5–8
Bastamy
3975
7.43%
Wheat, potato, perennial crops and vegetables
0–2
Bayazid
3525
6.59%
Pasture
2–5
Kharaqan
3550
6.64%
Wheat, barley and perennial crops
0–2
Khazaneh
9450
17.66%
Pasture
5–8
Khij
7225
13.50%
Pasture and annual crops (mainly wheat)
2–5
Mojen
1725
3.22%
Wheat and potato cultivation
0–2
Qaleh
4350
8.13%
Wheat and barley
0–2
Qehej
2800
5.23%
Wheat and pasture
2–5
The evaluation of methods tested produced
the following results: the Almagra model as an
application of maximum limitation method has
the highest correlation with the observed yield
of wheat and can be considered a sound method
for land suitability classification. This study
demonstrated that evaluation of the suitability
of lands using the Almagra model is accurate for
wheat cultivation in the region studied and that
this method can be used in other similar regions.
barley, corn and rice in Isfahan. Science and
Technology 3: 105–120 (in Persian).
Ayoubi S, Jalalian A (2010): Land evaluation
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Isfahan, Iran (in Persian).
Bazgir M, Givi J, Jalalian A (2000): Quantitative
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ACKNOWLEDGEMENT
The author would like to thank the Soil and Water
Research Institute of the Ministry of Agriculture
and also Agriculture Organization of Bastam for
cooperating in data collection for this research.
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