Generating of land suitability index for wheat with hybrid system aproach using AHP and GIS

Computers and Electronics in Agriculture 167 (2019) 105062

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Computers and Electronics in Agriculture journal homepage: www.elsevier.com/locate/compag

Generating of land suitability index for wheat with hybrid system aproach using AHP and GIS

T

Mert Dedeoğlua, , Orhan Dengizb ⁎

a b

Selçuk University, Agriculture Faculty, Department of Soil Science and Plant Nutrition, Turkey Ondokuz Mayıs University, Agriculture Faculty, Department of Soil Science and Plant Nutrition, Turkey

ARTICLE INFO

ABSTRACT

Keywords: Analytic hierarchy process Crop model GIS Land suitability Wheat

Today, crop models have been developed to products of strategic importance for precision agriculture management in the countries. The objective of this study was to generate the wheat suitability index (WSI) by using hybrid system that is including qualitative and quantitative reasons such as expert and sciencific knowledge weighted with analytic hierarchy process (AHP). This aproach was integrated into the GIS based on Linear Combination Teqnique in the study. For this purpose, the study was conducted in the field of wheat cultivation in Soğulca Basin with an area of 68.04 km2 located on Central Anatolia Region of Turkey. We have selected 10 criteria as physical, chemical and topographical that affects wheat cultivation in the basin which has been divided into 47 land units according to thematic soil map. With the WSI model, 32.05% of the study area was classified as highly and moderately suitable whereas, 67.95% of the total study area has marginally and not suitable properties for wheat cultivation. According to results, the most effective factors on the last score values for WSI were found soil depth, texture and slope indicators. The score values of the WSI were compared with 5 years (2013–2017) yields and NDVI values for testing of the model and it has been determined that land classification for wheat has been done with high accuracy for yield r2 = 0.83% and for NDVI r2 = 0.78%. The results of the study showed that the WSI was found as convenient model in semi-arid climate condition. However, we suggest that the WSI model should also be tested in similar climatic conditions and in different soil types in order to be available as a general - pass index. In addition, using AHP with GIS capabilities, we have high capacity to the integration of heterogeneous data for determination and classification of suitability in the agriculture areas.

1. Introduction In order to attain the self-sufficiency in agricultural production, it is necessary for developing countries to determine the suitability of their land for different agricultural types and to develop the product-based evaluation indices for this purpose (Chen, 2014). For countries with high population growth and migration potential such as Turkey the product based land evaluation approaches have additional importance. Due to 17.7 million tonnes annual production and covering the maximum agricultural area among cereals, bread wheat has got priority among these products in Turkey (FAOSTAT, 2011). Wheat has an important place in human nutrition and is considered as a strategic product (Mazid et al., 2009). Being one of the important sources of income for people living in rural areas of Turkey and providing the basic raw material for food industries, wheat has gained additional significance. The average bread wheat yield of Turkey, European Union and United

⁎

States has been determined as 2800 kg ha−1, 5760 kg ha−1 and 3540 kg ha−1 respectively (FAOSTAT, 2018). On the other hand, the mentioned these values are lower than mean values of both world and the advanced wheat growing countries. In order to increase the wheat production in the country and to reach the world average value, land use policies should be developed to support the sustainable rural development. Therefore, products based land evaluation studies should be conducted in Turkey, especially in the regions with intensive farming of wheat. Land evaluation is an absolute necessary process to determine the potential capabilities of land for different usages and obtaining the sustainable soil fertility (Mueller et al., 2010). The most efficient implementation of this process requires a multi-criteria approach that requires evaluation of many factors that affect each other, such as physical, chemical, morphological, topographical and climatic soil characteristics, by using expert opinion in terms of agricultural use (Iojă et al., 2014; Zhang et al., 2015; Mokarram and Mirsoleimani, 2018).

Corresponding author. E-mail address: [email protected] (M. Dedeoğlu).

https://doi.org/10.1016/j.compag.2019.105062 Received 24 April 2019; Received in revised form 16 August 2019; Accepted 18 October 2019 0168-1699/ © 2019 Elsevier B.V. All rights reserved.

Computers and Electronics in Agriculture 167 (2019) 105062

M. Dedeoğlu and O. Dengiz

Fig. 1. Location map of the study area.

decision-making analysis and determined that there are regions especially in steep slopes that have high coffee growing potential. In many studies, the capabilities and benefits of AHP/GIS techniques have been validated in the integration and visualization of spatial or non-spatial data obtained from product-based land evaluation studies of plants of strategic importance, such as wheat, beet, corn, tobacco (Mendas and Delali, 2012; Walke et al., 2012; Akıncı et al., 2013; Zhang et al., 2015; Mokarram and Mirsoleimani, 2018). However, no land evaluation model has been developed for wheat plants that are economically important for Turkey. Today, the public institutions producing agricultural policy in Turkey perform the land evaluation studies as suggested by Storie Index (Storie, 1978). In this method, all the characteristics rates are multiplied by each other. There is no difference among their effectiveness on crop growth and satisfying the land capability and suitability is controversial (Van Ranst et al., 1999; Sharififar et al., 2016). Other than the actively used Storie Index method in Turkey, alternative land evaluation methods are required to be investigated. In order to find a solution to this requirement, a novel model called Wheat Suitability Index (WSI) has been developed in this study for economically important wheat plants using the Hybrid Multi Criteria Decision-Making approach with AHP/GIS integration. The WSI method that is suitable for semi-arid climatic conditions of Turkey has been successfully tested.

Therefore in order to support decision-makers, multi-criteria decisionmaking models developed in 1960s have been presented as preferred approachs for land evaluation and agricultural suitability studies (Chavez et al., 2012). In recent years, Analytic Hierarchy Process-AHP (Saaty, 1980), which is a Multiple-Criteria Decision Analysis for the evaluation of land suitability is preferred in the evaluation of multi heterogeneous factors especially in case of strategically important plants (Ceballos-Silva and López-Blanco, 2003; Malczewski, 2006; Mandere et al., 2010; Chavez et al., 2012; Dengiz and Sarıoğlu, 2013; Akıncı et al., 2013). AHP is a decision-supported method that divides the complex multiple-factor problems into a hierarchical structure (Yang et al., 2008). At the same time, the inclusion of analytical models in GIS is reliably used by the researchers to assess the land suitability and allocation (Malczewski, 2006; Yalew et al., 2016). As a matter of fact, suitability classification has been made by using AHP/GIS according to different climate, soil types, nutrient content and topography factors of tobacco (Nicotiana tabacum L.) in China, and it has been stated that AHP model is the most effective method in determining the weights/significance of the factors (Zhang et al., 2015). Similarly Sarkar et al., (2014), in order to determine the fields suitable for wheat plants in India, the model was developed considering the precipitation data, soil depth, texture, drainage status, pH, organic matter content and slope factors using AHP model and suitability maps were produced by Weight Registration Analysis in GIS environment. Houshyar et al., (2014), evaluated the suitability of land for maize cultivation in terms of soil and climatic factors using AHP/GIS. As a result of the study, it was stated that suitable land for maize cultivation in FARS region of Iran is divided into 3 different suitability classes as good, medium and marginal land. Moreover, it was acclaimed that suitable areas provide greater yield with less usage of water and fertilizer. In addition, Özkan et al., (2019), conducted a study using the Fuzzy-AHP and TOPSIS in multi-criteria decision making approach in order to determine the areas suitable for paddy farming in the Bafra Delta Plain having semi-humid ecological conditions. The paddy farming compliance map obtained at the end of the study was compared with the land yield results and the results of the model were quite consistent. Nzeyimana et al., (2014) modeled the suitable cultivation areas for Arabian Coffee, which has the largest export volume of Rwanda, using GIS-based multi-criteria

2. Materials 2.1. Site descriptions Soğulca Basin is located in Central Anatolia region, Ankara (Capital of TURKEY) province, Haymana district 39° 25′ 7″ − 39° 20′ 8″ North latitudes, 32° 21′ 9″ − 32° 31′ 4″ East longitude (Fig. 1). The total area of the basin is 68.04 km2, the minimum and maximum height of the basin is 948 m and is 1382 m, respectively. Land usage type and spatial distributions of the study area are presented in Table 1 according to taxonomic subgroups. Nearly half of the area consists of grassland and the other half consists of dry agricultural areas. In Turkey, 30% of wheat cultivation is carried out in Central Anatolian region followed by Ankara with the second largest region of wheat cultivation (5.07%). In 2

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M. Dedeoğlu and O. Dengiz

micronutrients, was slightly alkaline. Similarly, the soil of the region contained high lime. Organic matter content, which is one of the most important indicators of soil quality, was sufficient in grassland areas but low in agricultural lands. According to FAO (1990), the available phosphorus (P) and potassium (K) contents of the soils of the region for alkaline reaction soils varied between very less to less. However, the nitrogen content (N) showed a wide distribution in the region and varied between low and medium (Kacar and Katkat, 2007).

Table 1 Land use type distributions in the study area. Soil Classes

Wheat (ha)

Pasture (ha)

Typic Xerorthent Typic Xerfluvent Typic Calcixerept Petrocalcic Calcixerept Lithic Xerorthent Lithic Haploxerept

879.5 67.04 991.86 187.48 1093.63 558.59

277.24 61.33 116.35 174.81 1710.6 688.1

Land use type

Area (ha)

Rate (%)

Dry farming Pasture (good) Pasture (weak) Pond Total

3778.1 1716.35 1312.08 75.81 6882.34

54.88 24.93 19.06 1.13 54.88

2.4. Yield values and satellite images

2017, average 2300 kg ha−1 bread wheat yield was obtained in Ankara. In the same year, 2250 kg ha−1 was realized in the district where the basin is located (TÜİK, 2018). According to the detailed soil survey report and map of the territory (Dengiz and Baskan, 2010), the experimental area of Central Anatolian region reflects typical soil characteristics with high calcareous, low organic matter content, high pH, stony and physiographic units consisting of alluvial deposits. At the same time, in order to represent different classes of land suitability in the basin, and due to the presence of land and intensive wheat cultivation, the Soğulca Basin has been selected for testing the land suitability for wheat model.

In the experimental region, wheat production is carried out in the area of 3778.1 ha. The 5-year (2013–2017) yield data of wheat fields grown in different mapping units were obtained from Ministry of Agriculture and Forestry and was confirmed with Farmers for WSI validation. According to the data obtained, the experimental region produced a maximum of 3490 kg ha−1, a minimum of 1020 kg ha−1 and an average of 2400 kg ha−1 wheat between the years 2013 and 2017. In addition to this, 5 images taken from 3 different satellite platforms belonging to July 2013–2017 July term were studied to determine the relationship between wheat suitability classes and NDVI (Normalized Difference Vegetation Index) values. In the studies conducted, it was stated that NDVI values derived from Landsat – 5, Landsat – 8 and Sentinel – 2A satellite images were useful and successful results were obtained in the estimation of plant yields (Ferencz et al., 2004; Herrmann et al., 2011; Skakun et al., 2018). For this purpose, 2013 July Landsat 5; 2014, 2017 July Landsat-8 and 2015, 2016 July Sentinel-2A multi-band raster images were used.

2.2. Climate

3. Method

For many years, climatic values of the region showed semi-arid climate features with hot and dry summers, cold winters and rainy seasons. According to the average values for last 20 years, the annual precipitation is 347.7 mm and the average temperature is 9.9 °C. The hottest month is July and coldest month is January with 21 °C and −2.1 °C, respectively. The rainiest month is May (51.4 mm) and the least rainy month is August (8.0 mm). According to soil climate regime of Newhall simulation model (Van Wambeke, 2000) the study site has Mesic soil temperature regime and Xeric (Dry Xeric in subgroup) moisture regime. Particularly, potential evapotranspration is higher than precipitation between April and October (Fig. 2). Soil needs irrigation particularly between May and July.

The process steps for generating the Wheat Suitibility Index (WSI) model, determining the suitability classes and the validation of these classes are presented in Fig. 3 as a flow diagram of the study. 3.1. Criteria selection, categorization of sub-factors and the weighting In the WSI model, Hybrid Systems were used in the selection of factors affecting plant growth. Hybrid Systems refers to the compilation of two types of models in the modern land evaluation approach. One of these models simulates the qualitative reasoning function while the other simulates the quantitative modeling section (De la Rosa and Van Diepen, 2002). In this study, research mentioned in literature was used together with Expert System as qualitative reasoning function and compliance criteria of sub-factors were evaluated. Employing several literature, 10 soil properties affecting the plant growth in wheat land availability index; depth, EC, slope, stone, pH, texture, organic matter, NPK content, aspect and lime have been determined according to the optimal soil requirements of wheat plants (De La Rosa et al., 1981; Huddleston et al., 1987; Barraclough, 1989; McVay et al., 1989; FAO, 1990; Rajaram et al., 1993; FAO, 1997; Soil Survey Staff, 1999; Arshad and Martin, 2002; Pathak et al., 2003; Jahn et al., 2006; Hazelton and Murphy, 2007; Iojă et al., 2014; Zhan et al., 2016; Mustafa et al., 2017; Aldababseh et al., 2018). For the classes created for each criterion, score values between 0 and 4 are assigned according to the possibility of growing of wheat plant. If the wheat plants were allowed and not allowed for optimum growth, criterion classes were given the values of 4 and 0, respectively. These two values were evaluated according to the classification factor and degree of growth. The quantitative modeling part of the study was carried out by AHP which is a multi-criteria decision making algorithm and the weighted scores were determined by taking the importance of the criteria for each other into consideration. For the evaluation of the selected criteria with AHP, vegetation demand of wheat plant has been taken into consideration with scientific expert approach. The selection of criteria for wheat land suitability assessment, categorizing into sub-

2.3. Soil properties In the study area, soils were classified as Lithic Xerorthent, Typic Xerfluvent, Typic Xerorthent, Lithic Haploxerept, Petrocalcic Calcixerept and Typic Calcixerept acoording to Soil Taxonomy (Soil Survey Staff, 1999). While, 59,9% of the total area was described as Entisol, due to low pedological process, 34,2% of it was evaluated in Inceptisol. The variation values of the selected factors in the WSI model were extracted from the thematic soil map and report of the region. The descriptive statistics based on the results of the physico-chemical soil properties of the research area and the detailed soil survey and mapping study carried out by Dengiz and Baskan (2010), are presented in Table 2. The soil depth in the study area showed a wide distribution between 13 and 117 cm. Most of the soils were heavily texture and their clay content were 50% in some regions. It had been reported that in all the field areas of the regions, there are different levels [less (2–5%), medium (5–15%) and high (15–20%)] of 6–20 cm stone problems. When the pH and EC values of the region were examined, the salinity problem was not determined according to Richards, (1954). However, the pH, which is directly related to the availability of macro and 3

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Fig. 2. Soil moisture and temperature regime diagrams according to Newhall simulation model.

3.1.2. Soil texture Soil textures were evaluated as a criterion in wheat land suitability index because of its effects on structure, infiltration rate and water holding capacity (Elsheikh et al., 2013). With no detailed quantitative data on the relationship of wheat plants with yield parameters in different textured soils (Chen et al., 2009), it is stated as a general opinion that high yield is obtained in loam soils (Ashraf et al., 2010; Ahmed et al., 2016). Together with the textural structure, it is also a parameter affecting the soil water content (Williams et al., 1983). Due to this, in the categorization of the texture parameters to the sub-factors, the soil was evaluated according to the wet condition and structure type during the year. FAO Soil Productivity Rating - SPR (Riquier et al., 1970), was used for this purpose. While categorizing the SPR textural groups to subclasses, soil was scored according to soil moisture content and structure. In this way, soil-plant-water relations will represent a criteria associated with both structure and climate values. Categorization of texture groups to sub-factors is presented in Table 4.

Table 2 Descriptive physical and chemical properties of soils. Properties −1

EC, dsm pH CaCO3, % Organic matter, % P2O5, mg kg−1 K2O, mg kg−1 N, % Sand, % Silty, % Clay, %

Min

Max

Mean

StDev

SE Mean

Variance

0.360 7.500 3.880 0.720 1.590 50.30 0.800 13.60 16.21 32.16

0.932 8.120 48.10 4.200 5.900 136.4 20.00 44.56 33.30 53.10

0.582 7.777 18.85 2.717 4.265 98.10 14.63 30.95 24.53 44.53

0.206 0.182 15.57 1.099 1.508 33.80 0.040 10.09 5.060 7.810

0.073 0.064 5.510 0.389 0.533 12.00 0.014 3.570 1.790 2.760

0.042 0.033 242.6 1.209 2.274 1145 0.001 101.9 25.59 60.94

factors and the requirement of the weighting methodology are presented below. 3.1.1. Effective soil depth Soil depth is the most important criterion of many agricultural classification systems for land evaluation, soil classification and soil quality parameter (Klingebiel and Montgomery, 1961; FAO, 1977, De La Rosa et al., 1981; Mueller et al., 2010; Aldababseh et al., 2018), and it is considered as a factor for wheat land suitability index in the study. Although the wheat plant can grow in different types of soils under favorable climatic conditions, it generally grows in the soil with best soil depth causing the effective root growth (Sarkar et al., 2014). For this reason, the depth ranges extracted from the thematic soil map of the region were categorized into sub-factors according to Soil Science Division Staff, (2017), and presented in Table 3.

3.1.3. Stoniness It is an important problem adversely affecting the moisture storage, infiltration of soil surface and lower horizons and limiting the land use (Miller and Guthrie, 1984). This situation causes disadvantage of wheat cultivation in the region and causes loss of productivity. According to thematic soil map of the study area, different levels of problems of stone have been identified in all of the regions. It was selected as an index criterion due to its direct impact on cultural practices and wheat yield and was categorized into sub-factors according to Soil Science Division Staff, (2017) (Table 5).

4

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Fig. 3. Shematic chart of the WSI generation. Table 3 Standardization of soil depth.

Table 4 Standardization of texture.

Sub Criteria of Depth (cm)

Score

Description

Sub Criteriaa

Score

0–25 25–50 50–100 100+

1 2 3 4

Gravel, sand, massive clay, peat Light textured soil Heavy-textured soil Medium-heavy soil: heavy Soil of average, balanced texture:

– fS, LS, SL, cS and Si C- > %45, C, SC, SiC C- < %45, C, CL, SL, SC, SiCL L, SiL and SCL

0 1 2 3 4

a fS: Fine sand, LS: Loamy sand, SL: Sandy loam, S: Sand, C: Clay, Si: Silt, SiC: Silty clay, cS: Coarse sand, SC: Sandy clay, CL:Clay loam, SiCL: Silty clay loam, L: Loam, SiL: Silt loam, SCL: Sandy clay loam.

3.1.4. Slope and Aspect In order to maintain vegetative growth, plants need sunlight at certain intervals during the growing period. Due to the association with root and shoot development, flowering, fertilization, photosynthesis and breathing, total yield and quality is directly affected with sun exposure (Bajracharya et al., 2013). The duration of this need varies according to the type of plant. However, in general, most culture plants

(cultigens) show optimum growth in southern and western directions that receive sunlight in a significant part of the day (Akıncı et al., 2013). For this reason, being related to sun exposure, field direction is an important factor affecting the plant growth and is considered as a 5

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Table 5 Standardization of stoniness.

Table 6 Standardization of slope and aspect.

Description

Score

Slope %

% 0–1 % 2–5 % 5–15 % 15–50 Bouldery Stones

4 3 2 1 0

Sub Criteria

Score

0–2 2–6 6–12 > 12

4 3 2 1

Aspect

criterion for land suitability assessment for wheat. Degree of slope negatively affects the irrigation and mechanization practices (FAO, 1977; Bajracharya et al., 2013). However, the increased degree of slope brings along the risk of erosion and this leads to organic matter and nutrient loss, especially in the soil (Sauer et al., 2010). For these reasons, it has been considered as a limiting factor for land capability in the land evaluation approach for wheat. Aspect and Slope data are extracted from contours (Fig. 4) generated from a 1: 25,000 scale topographic map of the region using the Spatial Analysis and 3D Analysis tools of ArcGIS 9.3 software. Classes and sub-factor scores according to the Slope and Aspect map are presented in Table 6.

Sub Criteria

Score

S, SW, SE W, E NW, NE N

4 3 2 1

Table 7 Standardization of EC.

3.1.5. Electrical conductivity (EC) EC is considered as a measure of the ion concentration of the soil solution (Richards, 1954). The EC, which is directly related to soil fertility, causes a decrease in vegetation and inhibits plant growth in later periods if it exceeds the critical values for plants (Bresler, 1972). Due to high concentrations of Na+ and Cl− under salty conditions, the nutrient imbalance results in a reduction in nutrient intake, including P, and ionoxicity (Miransari and Smith, 2007). In particular, similar to the study area, the semi-arid climate zones are a critical factor to consider for the field (Rhoades, 1996). EC, which is an important criterion for wheat cultivation, is categorized into sub-factors according to Francois et al., (1986), and presented in Table 7.

Sub Criteria EC (dS m−1)

Score

0–2 2–4 4–8 8–10

4 4 1 0

2014). Additionally, the pH directly or indirectly affects many physical, chemical and biological events occurring in the soil (Baridón et al., 2014). The appropriate pH range for the wheat plant is stated to be between 6 and 7.5. It has been observed that movement of phosphorus (P) and trace elements (Fe, Mn, Zn) in the soil is decreased under high pH, and in acidic soil, the availability of toxic elements to plants is increased (Leonard et al., 1976). In light of this information, pH ranges of the study area were categorized into sub-factors and presented in Table 8.

3.1.6. Reaction (pH) Soil reaction (pH) is an effective factor influenced by the nutrient uptake, solubility of toxic ions and microorganism activity (Liu et al.,

Fig. 4. Generation and distribution of slope and aspect map from contours in ArcGIS 9.3. 6

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Table 8 Standardization of pH.

Table 11 Classes and values of soil fertility index.

Sub Criteria of pH

Score

Sub Criteria

Soil Fertility Index

Score

> 8.2 < 5.5 5.5 – 6 6–7.5 7.5–8.2

1 2 4 3

Good Fertility Moderate Fertility Marginal Fertility Poor Fertility

> 80 80–50 50–20 < 20

4 3 2 1

Table 9 Standardization of organic matter.

SFI = Rmax ×

Sub Criteria of Organic matter (%)

Score

0–1 1–2 2–4 >4

1 2 3 4

A B × × 100 100

× 100

SFI = Soil Fertility Index, A+B+ +P R = Maximum ratio, 3 A, B… = Rating value for each diagnostic factor

(

)

The SFI values determined for each sample point are assigned according to the classes specified in Table 11 and categorized into subfactors.

3.1.7. Organic matter The organic matter content of the soil is directly influenced by plant cultivation as well as soil quality and sustainable soil fertility. Organic matter improves many physical and chemical properties in soils along with providing the basic nutrient and energy source in microorganisms (Bender et al., 1998; Riley et al., 2008). At the same time, it has been reported in studies that the yield and quality of wheat increases on organic matter applications (Guo et al., 2015). Because of this multifaceted functionality, it was selected as the criteria for wheat land suitability index and categorized to the sub-factors according to Ulgen and Yurtsever, (1984), and presented in Table 9.

3.1.9. CaCO3 Although soil CaCO3 content does not directly affect the yield and quality of wheat, it is stated that in calcareous soils phosphorous form compounds with Ca and hence, phosphorus uptake by plant decreases (Erdal et al., 2000). The CaCO3 content of the soil of the study area shows a wide distribution between 3.88% and 48.1% in topsoil and classified as calcareous to very calcareous. This situation necessitated the selection of lime content as index criteria in terms of wheat development and categorized into sub factors (Table 12), according to Soil Science Division Staff, (2017).

3.1.8. NPK contents Fertilizer use and management in plant production systems is very important. NPK fertilizers, which constitute the highest fertilizer input in wheat cultivation, are one of those (Liakas et al., 2001). Depending on the NPK content of the soil, it has been reported that the growth, yield and nutrient intake characteristics of wheat have increased significantly and the economic inputs due to fertilization have been reduced (Jiang et al., 2006). At the same time, important equilibrium relations between the optimum levels of these three nutrients were determined. It was reported that potassium significantly improves N and P intake in wheat grains, and when there is not enough K in the soil, increased use of N increases the K deficiency (Laghari et al., 2010). Soil fertility index (SFI) was used to better understand the effect of NPK contents and to integrate it into common score values (Mandal et al., 2005). SFI qualitatively calculates soil fertility classes with the parametric approach using the NPK parameters for each profile point (Dengiz et al., 2014). In the study, the factor values of the NPK contents in SFI calculation were determined according to scientific recommendations (FAO, 1988; Gürbüz et al., 2005; Laghari et al., 2010), and presented in Table 10. SFI is calculated and using the value of factor rating for each factor as follows:

3.2. Multi-criteria decision analysis In the process of determining the weight points for each of the parameters (Criteria), the weighted scores (Saaty, 2008) were determined by using the Analytic Hierarchy Process (AHS) technique, taking the importance of the criteria into consideration. This technique is a measurement theory based on the priority values obtained from the double comparison of the discussed parameters and allows the consideration of both quantitative (objective, quantitative) and qualitative (subjective, qualitative) factors in selecting the best alternative decision (Romano et al., 2015). The weighting of the criteria is carried out by the following process steps: (a) In the first step, matrices are formed in which binary comparisons are made considering the impact status of the criteria, Where A is the binary comparisons matrix; According to the element in the upper level of hierarchy, i is the importance of element according to element j (i, j = 1,2, …, n). Properties of the comparison matrix,

aji = 1/aij

Table 10 Standardization of NPK contents. Diagnostic factors

Available NPK A- Ntotal B- Pava C- Kexb a b

Units

g kg−1 mg kg−1 cmol(+)kg−1

Factor rating 100

80

50

20

10

> 3.2 > 80 0.28–0.74

3.2–1.7 25–80 0.74–2.56

0.9–1.7 8.0–25 0.13–0.28

0.9–0.45 2.5–8.0 > 2.56

< 0.45 < 2.5 < 0.13

Available. Exchangeable. 7

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M. Dedeoğlu and O. Dengiz

is presented below.

Table 12 Standardization of CaCO3.

CR =

Sub Criteria fo CaCO3 (%)

aij > 0

Sub Criteria

Score

0–1 1–5 5–15 15–25 > 25

1 2 4 3 1

(i, j = 1, 2,

Consistency check measures the logical inconsistency of judgements and allows the identification of errors that may occur in judgments. For the method to be valid, the consistency ratio should be 0.10 (10%) or less. If this ratio is greater than 0.10, the binary comparison matrices must be reconstructed (Saaty, 2008). Table 14 shows the weight values based on the paired comparisons for the 10 criteria selected for the determination of the suitability of the 47 mapping units in the study area to wheat cultivation. Consistency Ratio of the weights determined as a result of pairwise comparisons; TO = 0.09. This value is less than 0.10 indicates that the method is valid (Saaty, 2008). Otherwise, the pairwise comparison matrices must be reconstructed.

, n)

aik = aji; ajk (i, j, k = 1, 2,

, n)

Consistency formulation

If the binary comparisons are strictly consistent, the inputs of the matrix A binary comparisons will not contain any errors and will be expressed in the following equation;

3.3. Calculation of WSI

aij = W/W i j

WSI was computed in manner similar to those used in the linear Combination Models (Feizizadeh and Blaschke, 2013; Zhang et al., 2015), as follows:

Here, Wi, A is the priority value for the element i calculated by the matrix, Wj refers to the priority value for element j calculated by matrix A. Using the above equation, the following equation can be written,

n

S=

(i, j, k = 1, 2,

where S, total land compliance score; Wi, i the weight value of the parameter; Sub-criterion score of Xi, i; n is the total number of parameters considered. In the Linear Combination Technique (LCT), the criteria that affect the land use of agricultural land and weighted with AHP are multiplied by the lower value of the factor to which it belongs and score values between 0 and 4 are produced, because the subclass scores of the selected criteria get a minimum of 0 and a maximum of 4 points. The values for the selected and weighted criteria for the WSI calculation were manually added to the individual tables in the attribute table of the thematic soil map for each mapping unit, according to the properties of that soil unit in Arcgis 9.3 (Esri, Redlands, CA) GIS software. The histogram and distribution graph of the final score values obtained as a result of WSI are presented in Fig. 5. The data of overall suitability index were analyzed using descriptive statistical methods of Minitab 17 and the found the data had a means of 2.685, standard deviation of 0.3754, minimum of 1.9493, maximum of 3.4085, median of 2.6565, Q3 of 3.0129, and Q1 of 2.4324. Kolmogorov–Smirnov test produced a p > 0.01, which indicates that it is a normal distribution. In this study, suitability classification was based on the FAO framework (FAO, 1977) and modified slightly. Thus S1 represents that the land unit is highly suitable to wheat crop production with no limitations; S2 represents that the land unit is moderately suitable with some limitations; S3 represents that the land unit is marginally suitable with severe limitations; and N represents that the land unit is unsuitable for wheat growth (Mokarram and Mirsoleimani, 2018; Zhang et al., 2015). Based on this classification and data statistics, the WSI classification was assigned as presented in Table 15. The suitability map produced as a result of the WSI classification is presented in Fig. 6 and the suitability classes and spatial distributions of each mapping unit are presented in Table 16.

, n)

(b) Calculation of the priority of each of the parameters was compared after the creation of matrix A (the maximum eigenvalue vector or priority vector or weight values of the criteria): Step 1: The values in each column of the binary comparison matrix are collected. Step 2: Each element in the binary comparison matrix is divided by the total value of the column in which it is located. The resulting matrix is called the normalized binary comparisons matrix. Step 3: The arithmetic mean of the elements in each row of the normalized binary comparisons matrix is calculated. These arithmetic mean values provide an estimate of the relative priorities of the compared elements. (c) The final step of the method is the consistency check of the obtained eigenvector. The binary comparisons matrix (A) is multiplied by the priority vector (W) to yield a new vector. A second new vector is reached by dividing each element of this new vector by the corresponding value in the priority vector. The maximum eigenvalue (λmax) is estimated by taking the arithmetic mean of the values of this last vector. The closer the λmax to the number (n) of the binary comparisons matrix, the more consistent the results will be (Boroushaki and Malczewski, 2008). If the A binary comparisons matrix is not fully consistent, the value λmax will deviate from n and the other eigenvalues will deviate from zero. These deviations are determined by the Consistency Index (CI) given below.

CI =

(Wi ·Xi) i=1

ajk akj = Wi/Wj = aij

ajk·akj = W/W i j = aij

CI RI

3.4. Derivation of NDVI values

max n n 1

Today, computing technologies that is combined with GIS and Remote Sensing software enabled applications such as estimates of crop biomass (Manna et al., 2009). Especially, remote sensing imaging is considered one of the main sources of information about the land vegetation (Campbell and Wynne, 2011). The vegetation status is concerned with the development of plants and it is directly related to the crop potential yield of the soils (SYS et al., 1991). Therefore, the

On the other hand, Random Index (RI) values should be known in order to calculate the Consistency Ratio (CR). These values were generated by randomly filling 100 matrixes in each dimension of 1–15 dimensional matrices and taking the average of the Coherence Indexes calculated according to the above formula (Table 13). The CR equation 8

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M. Dedeoğlu and O. Dengiz

Table 13 Values of random index (RI). n

1

2

3

4

5

6

7

8

9

10a

11

12

13

14

15

RI

0.00

0.00

0.58

0.90

1.12

1.24

1.32

1.41

1.45

1.51

1.52

1.54

1.56

1.58

1.59

a

The RI values for 10 criteria is 1.51.

Table 14 Pair wise comparison matrix and eigenvector of criteria in AHP. Pairwise Comparison Matrix Criteria

Depth

Slope

EC

Stony

Texture

OM

pH

NPK

Aspect

CaCO3

Depth Slope EC Stony Texture OM pH NPK Aspect CaCO3 Total

1.00 0.50 0.33 0.33 0.33 0.33 0.33 0.20 0.33 0.20 3.90

2.00 1.00 3.00 0.50 0.50 0.33 0.33 0.33 0.33 0.20 8.53

3.00 0.33 1.00 0.33 0.33 0.33 0.33 0.33 0.33 0.33 6.67

3.00 2.00 3.00 1.00 3.00 0.33 0.50 0.50 0.33 0.50 14.17

3.00 2.00 3.00 0.33 1.00 0.33 3.00 0.33 0.50 1.00 14.50

3.00 3.00 3.00 3.00 3.00 1.00 3.00 0.50 0.33 0.50 20.33

3.00 3.00 3.00 2.00 0.33 0.33 1.00 0.33 0.50 0.50 14.00

5.00 3.00 3.00 2.00 3.00 2.00 3.00 1.00 0.33 0.50 22.83

3.00 3.00 3.00 3.00 2.00 3.00 2.00 3.00 1.00 0.50 23.50

5.00 5.00 3.00 2.00 1.00 2.00 2.00 2.00 2.00 1.00 25.00

Normalized Pairwise Comparison Matrix Criteria

Depth

Slope

EC

Stony

Texture

OM

pH

NPK

Aspect

CaCO3

Depth Slope EC Stony Texture OM pH NPK Aspect CaCO3

0.26 0.13 0.09 0.09 0.09 0.09 0.09 0.05 0.09 0.05

0.23 0.12 0.35 0.06 0.06 0.04 0.04 0.04 0.04 0.02

0.45 0.05 0.15 0.05 0.05 0.05 0.05 0.05 0.05 0.05

0.21 0.14 0.21 0.07 0.21 0.02 0.04 0.04 0.02 0.04

0.21 0.14 0.21 0.02 0.07 0.02 0.21 0.02 0.03 0.07

0.15 0.15 0.15 0.15 0.15 0.05 0.15 0.02 0.02 0.02

0.21 0.21 0.21 0.14 0.02 0.02 0.07 0.02 0.04 0.04

0.22 0.13 0.13 0.09 0.13 0.09 0.13 0.04 0.01 0.02

0.13 0.13 0.13 0.13 0.09 0.13 0.09 0.13 0.04 0.02

0.20 0.20 0.12 0.08 0.04 0.08 0.08 0.08 0.08 0.04

Eigenvector Criteria

Normalized Sum of Rows

Depth 2.27 Slope 1.40 EC 1.75 Stony 0.87 Texture 0.90 OM 0.59 pH 0.93 NPK 0.50 Aspect 0.42 CaCO3 0.37 λmax = 11.35; CI = 0.15; CR = 0.09

Normalized Average Rows

Eigenvector

2.27/10 1.40/10 1.75/10 0.87/10 0.90/10 0.59/10 0.93/10 0.50/10 0.42/10 0.37/10

0.227 0.140 0.175 0.087 0.090 0.059 0.093 0.050 0.042 0.037

compatibility of the land evaluation methods is compared with the yield values in many studies (Brinkman and Smyth, 1973; Hall and Wang, 1992; Sharififar, 2012). With the latest technological developments on applied of remote sensing, we have been obtaining about the product yield of lands. The most common remote sensing technique used for this purpose is the vegetation indexes (Al-doski et al., 2013), and the most widely used vegetation index is Normalized Difference Vegetation Index (NDVI) (Tucker, 1979; Groten, 1993; Garrigues et al., 2007; Azzari et al., 2017). NDVI is sensitive to active photosynthetic compounds and is therefore a popular way to measure the productivity of vegetation, or “greenness,” in a defined area (Tucker, 1979), as follows:

NDVI = (NIR

RED = Visible red band We used Erdas Imagine 9 (Geosystems, 2005) to perform NDVI analysis for estimation of wheat yield from canopy reflectance to biomass and ArcGis 9.3 (ESRI 2010) software was used to store data and generate thematic maps. 3.5. Test of validation for the WSI The yield values of the regional lands were extracted according to the fields in the WSI map using the Dissolve tool of ArcGIS 9.3 software and the average values to represent suitability classes were derived. Similarly, the WSI map with a raster layer NDVI map derived from satellite images was overlapped and averaged by subtracting the NDVI values corresponding to the suitability classes. Linear regression analysis was performed with WSI score and the reliability of the index was tested.

RED )/(NIR + RED)

where NIR = Near infrared band 9

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M. Dedeoğlu and O. Dengiz

Fig. 5. Histogram and normality test graph of WSI scores.

of the region. In fact, it has been reported in the past studies that the physical and topographic characteristics of the lands were more effective for the final score value of the agricultural eligibility classes calculated according to the LCT model (Patrono, 1998; Mueller et al., 2010; Dengiz and Sarıoğlu, 2013; Ahmed et al., 2016). Similarly, it has been reported that different agricultural suitability assessment models establish functions at a higher rate than the physical and topographical characteristics of the lands, and this situation is caused by non-economic conditions such as soil depth, slope and texture, and these characteristics are the potential characteristics of the lands (Briza et al., 2001; Hemadi, 2011; Aldababseh et al., 2018). Therefore, it is stated that the parameters of potential characteristics such as land evaluation, soil classification and soil quality are the most important criterion of many agricultural classification systems (FAO, 1977; Mueller et al., 2010; Sharififar, 2012). At the same time, the stoniness, whose effect can be reduced or diminished, was an important factor requiring the evaluation of the different classes of land in the wheat suitability index

Table 15 Distribution of suitability classes under the four categories. Suitability

Class

Area (ha)

Area (%)

WSI index

Highly suitable Moderately suitable Marginally suitable Not suitable

S1 S2 S3 N

1223.88 957.64 1548.94 3076.07

17.98 14.07 22.76 45.19

3.02–4 2.69–0.3.01 2.44–2.68 0–2.43

4. Result and discussions 4.1. Effect of criteria’s to WSI As a result of weighting of factors by AHP technique, soil depth (2.27%), slope (1.40%) and texture (0.90%) were set as the effective determinant of agricultural potential, and it was determined that significant differences were obtained according to the changes in the soil

Fig. 6. Map of WSI in Soğulca Basin. 10

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M. Dedeoğlu and O. Dengiz

for land-based assessment. There is no salinity problem in the soil of the region and the effect on all mapping units is equal, but the soil salinity is a problem (Van Noordwijk and Cadisch, 2002) which can occur as a result of excessive fertilization in the areas where intensive agricultural activity is carried out, such as the study area, and should be checked at regular intervals. The pH change of the region (7.5–8.12) was basic slightly alkaline and the lower factor values were modeled with 3 and 4 multipliers. The dramatic absence of the pH change interval did not cause any significant differences in the area of wheat cultivation. The organic matter content of the study area (0.72%−4.20%) shows a wide distribution in the deficient-sufficient classes. This situation affects the final score values of the eligibility classes at different rates. However, in wheat cultivation, organic matter was considered as a soil regulator and the index effect was weighted as 0.59%. For example, the mapping unit, which has sufficient organic matter content and is modeled with a factor of 4, but has an effective soil depth of 25–50 cm, has been defined as marginally suitable; whereas the mapping unit with similar organic matter content and a soil depth of 50–100 cm has been described as moderately suitable. This situation is an indication that high organic matter is not significant alone for plant cultivation (Riley et al., 2008). According to SFI model of soils NPK values were determined as the good and marginal fertility. The fact that agricultural activities are being carried out actively in the agricultural area of the region is an important reason for not having any problem in terms of NPK scope. At the same time, NPK coverage can be increased with fertilizer applications at different times during the vegetation period. Therefore, 0.50% of NPK coverage is the easiest factor in improving the ration of weight values. However, in order to present the current situation and proposing the measures to be taken, it is important to function in the wheat suitability index. The aspect factor was indexed to determine the area with optimum characteristics in wheat cultivation and weighted as 0.42%. In this way, it is suggested to plan considering the need of sunlight during the planting period of wheat. However, this aspect is not vital for wheat cultivation and is important for proper vegetation development. Therefore, it is appropriate that the contribution of the index in total factor weights is lower than other factors (Akıncı et al.,

Table 16 Distribution of land suitability classes of Land Units based on linear combination technique. Units

WSI Value

Class

Areas (ha)

Land Units

WSI Value

Class

Areas (ha)

LU1 LU2 LU3 LU4 LU5 LU6 LU7 LU8 LU9 LU10 LU11 LU12 LU13 LU14 LU15 LU16 LU17 LU18 LU19 LU20 LU21 LU22 LU23 LU24

3.187 2.140 3.042 2.903 3.409 2.157 1.949 3.240 3.187 2.420 2.822 2.586 2.559 2.446 3.013 2.089 2.347 3.092 2.751 2.952 2.666 3.179 2.559 2.193

S1 N S1 S2 S1 N N S1 S1 N S2 S3 S3 S3 S2 N N S1 S2 S2 S3 S1 S3 N

156.02 105.54 147.17 152.85 27.55 1651.30 211.52 229.65 67.91 22.21 58.77 16.50 111.80 522.27 204.75 229.68 283.33 204.73 42.57 196.86 123.14 68.01 112.71 61.37

LU25 LU26 LU27 LU28 LU29 LU30 LU31 LU32 LU33 LU34 LU35 LU36 LU37 LU38 LU39 LU40 LU41 LU42 LU43 LU44 LU45 LU46 LU47

2.089 3.318 3.256 3.092 2.437 2.506 2.368 3.013 2.450 2.647 2.700 2.595 2.890 2.766 2.766 2.786 2.873 2.506 2.089 2.524 2.420 2.536 2.089

N S1 S1 S1 S3 S3 N S2 S3 S3 S3 S3 S2 S2 S2 S2 S2 S3 N S3 N S3 N

144.30 108.50 83.84 130.50 68.14 189.59 45.85 23.38 20.86 18.66 33.46 79.97 37.18 90.60 45.30 76.19 29.19 122.34 127.70 94.07 193.28 35.41 144.30

with a weight value of 0.87%. The EC factor, which is the indicator of soil salinity, is functioned with the highest chemical soil property coefficient with a value of 1.75%. As a matter of fact, soil salinity is a factor that directly affects the plant development (Miransari and Smith, 2007), and if it is above 8 ds m−2 except for some special aquaculture, it resolves the model in almost all parametric land evaluation approaches and requires the evaluation of the land in non-agricultural classes (Verheye, 2009). Therefore, the products with the highest coefficient of chemical factors in wheat suitability model are suitable

Fig. 7. Distribution of WSI classes in Sentinel 2A B8-B4-B3 (NIR-RED-GREEN) band combination false color satellite image and wheat field in not suitability areas. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) 11

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M. Dedeoğlu and O. Dengiz

directly affect wheat cultivation.

Table 17 Suitability classes and yield values corresponding to NDVI values. Years

Yield (kg ha−1)

WSI Scores

Means of NDVI values

2017

3150 2880 2500 1450

3,201 2,866 2,55 2,205

0.780 0.774 0.491 0.125

2016

2840 2550 2300 1160

3,201 2,866 2,55 2,205

0.800 0.785 0.533 0.425

2015

3490 3130 2750 1680

3,201 2,866 2,55 2,205

0.934 0.800 0.559 0.488

2014

2980 2750 2230 1180

3,201 2,866 2,55 2,205

0.812 0.805 0.672 0.412

2013

2990 2850 2170 1020

3,201 2,866 2,55 2,205

0.776 0.774 0.499 0.149

4.2. Land suitability for wheat

Maps

As a result of the study, the plots showing distribution in all suitability classes of Soğulca Basin were determined. According to the generalized WSI map, 1223.88 ha area (17.98%) is classified as highly suitable in the study area. The criteria taken into consideration for wheat cultivation in these areas have no restrictive effect. In the regional land, 957.64 ha (14.07%) area is defined as ‘Moderately suitable’. These areas have a soil depth of 50–100 cm and are close to flatlevel slopes. On the other hand, the heavy soil texture, low organic matter and high CaCO3 content and the stoniness problems which can be stand out as the limiting factors for agricultural production. In addition, these negative factors also reduced the value of WSI. As a result of the index classification, 1548.94 ha (22.76%) land was determined as marginally suitable. The combinations of the factors used in wheat suitability index in these domains were found to be remarkable. As a matter of fact, although some terrains have a flat slope, high organic matter content and soil depth of 25–50 cm, they were considered as marginally suitable due to presence of high pH and CaCO3 along with the 5–15% stoniness problem. Similarly, regions with 2–5% stony density, high organic matter and 5–15% CaCO3 content along with a soil depth of 0–25 cm, slope of 6–12% and aspect of N-NE were included in this category. In the study area, 3076.07 ha (45.19%) was determined as not suitable for wheat cultivation. At the same time, the not suitable class shows the largest spatial distribution in the research area. In these areas 0–25 cm soil depth, slope greater than 12% and problem of 15–50% stone are the main limiting factors of wheat cultivation and also, other factors with high score values could not be included in the appropriate classes for wheat cultivation. According to ground controls and satellite imagery, it has been determined that these areas are mostly used for pasture purposes. However, it was observed that wheat cultivation was tried in some regions (Fig. 7). 4.3. Validation of the WSI The score values of the wheat suitability classes determined as a result of the index calculation were compared with 5-years (2013–2017) product yields and NDVI values (Table 17) by using the linear regression analysis. For the evaluation of the data, it was determined that for yield r2 = 0.83% and for NDVI r2 = 0.78%, respectively (Fig. 8). The obtained validation numbers showed that the use of the combination of Expert system - AHP in the lower factor weighting of the selected criteria in the WSI gave reliable results. In many studies conducted on product-based land evaluation modeling, uncertainty of decision has led the experts to fuzzy logic theory approach and successful results have been obtained (Malczewski, 2006; Sharififar et al.,

2013). CaCO3 content with 0.37% contributes to the lowest weight value among the index factors. This situation has reduced the importance of CaCO3 content in double comparisons because it does not

Fig. 8. The relationship between the yield and NDVI values of WSI scores. 12

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M. Dedeoğlu and O. Dengiz

2016; Aldababseh et al., 2018). In our study, the optimum conditions for wheat cultivation were in accordance with both the expert opinion and literature. As a matter of fact, AHP depends mainly on the subjective evaluation of the relative importance of the two factors and the consistency of the overlap of the knowledge from the literature and the expert opinion provides consistency in determining the relationship between each factor (Saaty, 2008). In addition, similar to the previous studies, the LCT model has been found to be successful in the calculation of the criteria in different units standardized according to the hybird system and in its integration with GIS (Malczewski, 2004; Feizizadeh and Blaschke, 2013; Zabihi et al., 2015). A successful land evaluation model should be able to identify the potentials that can be reached and the measures that can be taken in addition to identifying the current potential (FAO, 1977; Radiarta et al., 2008; Verheye, 2009). Such hypothetical approaches can be made with the WSI model. For example, in the case of the problem of stoniness, the LU21 coded area is assigned from the moderately suitable to the marginally suitable class and the productivity potential of the land can be determined. In particular, some part of the study area which has deep soil was classified as marginally suitable for agricultural activities due to some physical limiting factors such as heavy soil texture or stoniness. In order to eliminate or reduce of negative physical factors’ effect, it should be taken some measures and applications such as animal or green manure addition in order to increase the amount of organic matter. Thus, it can be reduced the negative effects of heavy body (Clay > 45%) to plant and soil-water mobility (Riley et al., 2008). Moreover, it is determined that sowing period can be programmed according to the seasonal conditions of that year by taking into consideration the aspect status and thus, wheat cultivation can be done economically. At the same time index can be easily modified for different terrain features. WSI has been tested in a basin of wheat cultivation in dry conditions under continental climate conditions. However, the drainage factor can also be evaluated in the irrigation areas (Albaji and Hemadi, 2011), and in this case the effect of other factors will be different in the binary comparison step (Aldababseh et al., 2018).

yields in similar lands. For this reason, beside the efficiency parameters of the region, the capabilities of the satellite images were also utilized. In this way, the high relationship between canopy reflectance values and suitability classes has opened a separate window for the similar studies, and these results presented that a different option can be used for model validation. We suggest that the WSI model should also be tested in the similar climatic conditions and in different soil types in order to be available as a general - pass index. Thus, the effect of pH, EC and CaCO3 will be better understood on score values and suitability class assignments and WSI model could be able to fully represent the semi-arid regions. Appendix A. Supplementary material Supplementary data to this article can be found online at https:// doi.org/10.1016/j.compag.2019.105062. References Ahmed, G.B., Shariff, A.R.M., Balasundram, S.K., Fikri bin Abdullah, A., 2016. 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5. Conclusions In order to determine the land suitability for wheat cultivation, the success of the WSI was determined by a practical product-based land evaluation approach integrated with GIS. WSI has been also tested with both ground validation such as yield parameters and satellite images using NDVI values in the Central Anatolia region. In addition to that, it has been determined that suitability classification for wheat has been performed with high accuracy. Moreover, suitability classes of wheat were mapped using GIS capabilities in a clear and understandable way. It is recommended that the use of ArcGIS 9.3, which is a GIS software, is appropriate tool for the analysis of research data and for the scientific mapping of the land suitability for wheat farming. In this study, the criteria weighted with AHP in the WSI model were evaluated under three main headings: physical, chemical and topographical. High level of topographical and physical criteria of wheat cultivation provided a reliable index approach according to the validation coefficients. In the weighting of factors by using AHP has been maintained consistency when literature and expert knowledge are compatible. The study also showed similarity with previous research findings in which AHP has high capacity for the integration of heterogeneous data. Therefore, it is recommended to utilize WSI for wheat cultivation in different regions with similar climates in Turkey to identify the scientifically defined land. At the same time, this investigation has shown that decision makers and policy makers can use the WSI approach as an alternative to the Storie Index. In this current study, wheat yield values of the soils which are under traditional farmer management were used for WSI model test. However, different cultural practices cause variable 13

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