Comparing Parametric Methods (the Square Root and the Storie) with the Fuzzy Set Theory for Land Evaluation of Khaje Region for Wheat

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1 International Journal of Advanced Biotechnology and Research (IJBR) ISSN , Online ISSN X, Vol-7, Special Issue4-May, 2016, pp Research Article Comparing Parametric Methods (the Square Root and the Storie) with the Fuzzy Set Theory for Land Evaluation of Khaje Region for Wheat Elham Ghanbarie 1*, Ali Asghar Jafarzadeh 1, Farzin Shahbazi 1 and Moslem Servati 2 1 Soil Science and Engineering Department, Agriculture Faculty, University of Tabriz 2 Shahid Bakeri High Education Center of Miandoab, Urmia University, Iran ABSTRACT The evaluation of land suitability is used to determine compatibility for a specific type of land. In this regard, different approaches to land evaluation have developed including FAO (parametric ), which is the most widely used method and has greater importance than other methods. The aim of this study was to evaluate the suitability of some lands of Khaja region for irrigated wheat crop with parametric methods ( Storie and Root ) and Fuzzy Set Theory and to compare these methods. The main goal of this research is to introduce of the preferred method for this area.to achieve the goals field and laboratory data were obtained from 10 land units then soil indices were calculated using parametric (square root and Storie) and fuzzy methods. The results showed that in calculating the soil indices fuzzy method presented higher and better land suitability than the parametric methods (Storie and square root).the coefficients of correlation between land indices and observed yield are respectively 0.76, 0.85 and 0.91 for square root and storie and fuzzy method.it can be concluded based on the above mentioned results that the obtained indices by fuzzy method predict the observed operation better than other methods due to higher coefficients of correlations. Key words: Fuzzy method, land indices, parametric method, suitability evaluation, wheat INTRODUCTION Soil application as a part of the environment and as the main platform of organisms' growth should be based on scientific and right methods to be used in producing agricultural productions and as a stable source in providing human requirements. For this reason spreading the destruction of this finite resource can threat world food security and quality of environment (Blanco and lal,2008). In an effort for optimal utilization of soil different evaluation methods have been suggested that parametric and fuzzy are some of them. Yasmina et al. (2001 )used parametric methods for assessment of land evaluation in Morocco and showed that most lands have critical suitability of and texture, depth and drainage were the most important factors limiting the growth of the crop under study. Since the classical land evaluation methods such as FAO had problems in reflecting the spatial variability of soil properties and led to the loss of a significant fraction of data ( Mohammadi, 2007 in persian) and require quite a lot of time and money and don't consider average classes (Bakhtiarifar et al ), to eliminate this deficiency the modern methods in land evaluation, such as the theory of fuzzy collections can be used. Using the theory of fuzzy systems for land evaluation by Burrough (1989 ) showed that in the classic systems characteristics of lands as input of system are mainly definedin vague terms such as relatively low erosion, poor

2 drainage, etc. (the uncertainty, ambiguity) and in such situations, operated classification leads to the loss of a significant fraction of useful information, therefor the use of models that are software -based collections such as fuzzy approach is important. Fuzzy system is defined by membership functions and membership functions range between zero and one, represents a continuous, gradual increase of the non-member to full membership and prepares the possibility of quantitative description and continuous variability processing along or the of natural systems uncertainty, including soil systems and various properties of it for environmental scientists ( Mohammadi, 2007 in persian). Since soil indices are used in land evaluation and prediction of land production potential by quantitative methods, it's necessary to determine which of the indices of storie or the square root and fuzzy methods presents better result and which one is closer to reality for predicting the production of this region. In this research the soil indices were calculated by square root and stories for suitability evaluation of Khajeh lands in East Azarbajan province and the obtained results were compared with indices obtained by fazzy method. METHODS AND MATERIALS The position of the area under study The area under study with an area of 1 hectares, is located in northeast of Tabriz and begins from east Khajeh and continues to Sakhsloue. The area is located in range of 46 37' 35.63" to 46 42' 30.13"east longitude and 38 7' 43.42" to38 9' 24.51" north latitude (fig.1). The annual average temperature of the area under study has been 13.58C during the years of 1991 and Heat and humidity regime are in order mesic and aridic border to xeric and the studied region is among the Steppe areas with cold winter and relatively warm summer according to Geosen,climate dividing method. The classification of witness profiles was performed by American classification key ( 2014). The results showed that the soils of the studied area have been located in Aridi soils order and Calcids, Cambids, Gypsids, Salids suborders according to American classification key. Figure1: Location of the study area (East Azarbaijan, IRAN) Qualitative land suitability evaluation by parametric method (Storie and Square root) In order to study the land suitability evaluation, 10 land units were selected in the area under study. Since in wet areas the properties used for land suitability evaluation are climate, topography, drainage, texture, coarse particles, depth and three characteristics related to soil fertility (appearance cation exchange capacity (ACEC), alkalization saturation or ph and organic carbon) and for dry regions, characteristics of gypsum,caco3, salinity and alkalinity are replaced with the characteristics related with fertility. Thus in this study, according to the conditions of the studied area properties which were used for dry regions were used to calculate the land indices. It is worth noting that except flooding, drainage and topography properties, the weighted average and depth coefficients up to cm deep (depending on beeing annual of wheat ) were used to calculate other properties (Sys et al., 1991a).Then these characteristics were Elham Ghanbarie, et al. 344

3 compared with growing requirements of irrigated wheat based on Sys et al (1993 ) tables, and the suitability degree of each property was calculated and at last the uncorrected land indices (LI) were calculated for each unit using equation 1 (Khidir, 1986) and 2 (Storie, 1976). In these equations A, B, C and are suitability degrees specialized to each of land properties and R min is the minimum suitability degree of properties. Then the uncorrected indices were changed to corrected indices by equations 3 to 7 for storie and 8 to 12 for square root methods (sys et al., 1991a) (1) (2) If the least suitability degree of 8 properties is 85 or more, the equation of class will be used and if it is 60 to 85 the equation of class, 40 to 60 the equation of class, 25 to 40 the equation of class N1, and less than 25 the equation of class will be used. In these equations the CL 1 is correctedland index, SLI 2 is uncorrected land index by storie method and SQRI 3 is Uncorrected land index by square root method. Finally land suitability class was determined by using Table 1 for corrected and uncorrected land indices. Table 1: Land index values for land suitability classes (Sys et al., 1991a) Suitability classes Land indices (Highly suitable) = 75- (Moderatly suitable) = (Marginal suitable) = (Actual unsuitable) =N (Permenet unsuitable) = Corrected Land Indices 2 Storie Land Indices 3 Square Root indices Correction equation class (3) (4) (5) N1 (6) (7) (8) (9) (10) N1 (11) (12) The application of fuzzic method: Collections of fuzzic theory involve the following steps: 1. The choice of properties of evaluated lands for the studied crop 2. The choice of the appropriate membership functions 3. The choice of apropriate weights for showing the relative importance of various characteristics on the studied crop. 4. The choice of final synthesizer equation for evaluating the final suitability of lands. In this study to evaluate the theory of fuzzy collections the MATLAB software version (R2010a) was used for programming RESULTS AND DISCUSSION A series of steps is essential to carry out the qualitative land suitability evaluation by parametric method. The parametric method includes the following steps ( Sys 1991b): 1 determining the growing period and the subsidiary period for the studied crop in the studied region 2 - evaluating the climate using the regional climate data 3 - evaluating the soil and landscape based on obtained information from field and laboratory studies Elham Ghanbarie, et al. 345

4 4. calculating the land index by combining the results of climate, soil and landscape Table 2: Study of wheat growth cycle in the region Growing cycle length 171 Growing cycle 23 September - 6October+1March- 16Julay Harvest 16Julay Ripping stage 5May-11Julay Floweringe stage 10 Apri-5May Vegetation Stage 23September- 6October+1Ma rch-10april Utilizasion stage Table 3: Mean Climatic characteristics of the meteorological station of Tabriz for 20 years and their Suitability indices for wheat Parametric methods Climate data Climate Charactristics in growing cycle Mean temprature of growing cycle Mean temprature of vegetationstage Mean temprature of flowering stage Mean temprature of Ripping stage 0.6 Minimum Mean temperature of coldest month 11 Maximum Mean temperature of coldest month After calculating and gaining the parameters of climate (Table 3), the climatic suitability degree and its suitability class was determined. As it is seen, climate does not make any limitation for irrigated wheat. To determine the degree of soil and landscape suitability, first the weighted average values of properties that are effective in determining the land suitability of wheat were calculated using different soil depths weighting coefficients table for each land unit. Then the suitability degrees of different land units (Table 5) were calculated for the studied crop based on comparing the soil and landscape properties and soil and landscape requirements table which was suggested by Sys et al.(1993). On a local scale that climate change is low, the relief properties are the determining factors of successful cultivation. Relief is directly effective on development and growth through its impact on water availability in various sectors of landscape and indirectly through affecting on the soil's physical and chemical properties such as OM, alkalization saturation persentage, soil temperature and particle size distribution (Jiang &Thelen, 2004). In Khajeh region it could be reported that salinity, alkalinity and gypsum surplus in different land units are the most important limiting factors in land suitability of studied region for the selected crop. Jafarzadeh et al. (2008) reported the texture as one of the major limiting factors of Bilverdy area for corn pellets. Finally, land indices were calculated by using equations 3 to 12 and with suitability degrees of different lands Incorporation, and the land suitability class was determined using the obtained indices (table6). Table 4: Cliamate rating and suitability classes for maize by Storie and Square root methods Classes Square root mean classes Storie Crop Wheat Wheat Table 5: Suitability classification of soil and landscape data for Wheat Salinity andalkality Gypsum Lime Depth,Texture and Coarse fragment Drainage Flooding Topoghraphy Land Uunits Elham Ghanbarie, et al. 346

5 Table 6: Land indices and land suitability classes for 10 land unites of the study area for Wheat based on different methods Land unit Observed Yield (kg/ha) Suitability classes Corrected Square Root mean land indices Suitability classes The figures 2 and 3 show the land suitability map of studied region for studied crop. Correctedstorie land indices fig. 2 qualititive land suitability classes by FAO- square-root model for wheat fig. 3 qualititive land suitability classes by FAO- storie model for wheat In the following lines the regression relations between calculated land indices with stories and square root method and observed production are shown in figures 4 and 5. The obtained regression coefficient in these methods is significant (p>1%) and this subject shows the high accuracy of parametric method for the mentioned crop in this region. Elham Ghanbarie, et al. 347

6 Figure 4- relationship between calculated land indices by square root method and observed Figure 5- relationship between calculated land indices by storie method and observed production production (Kg/ha) for wheat (Kg/ha) for wheat Qualitative land suitability evaluation showed that for irrigated cultivation of the studied crop excess salinity and gypsum have created the greatest limitation in most of the units. Although salinity is modifiable, more studies and investments are needed to be done. This region is very suitable for the establishing the irrigation and drainage network in terms of geomorphology. Ajichay river which passes among the region has created good position for unloading the drains and their outputs. Farajnia (2007in Persian) in Marand region and Malekian feat Jafarzadeh (2009in persian) in research station of Khajeh reported that the salinity during profiles route are the most limitation factor in the land under study suitability evaluation. There is no limitation in terms of physical properties (texture, coarse particles and depth) in the studied region. Land suitability evaluation by using Fuzzy Set Theory collections 1-The choice of effective properties in production of the studied crop: In this method 12 characteristics (slope, coarse particles, soil depth, CaCO3%, gypsum%, ph, OC, EC, ESP, temperature rate during growth period and average of humidity percentage) according to Sys et al. tables (1993) that are effective in the rate of studied crop production were selected. 2-The choice of membership functions and the formation of characteristics matrix In this study Kondol membership functions (tang et al.1991) were used for determining the membership degree of each land characteristics for land suitability classes based on the studied crop requirements and the results were put in the matrix that named R. 3-Weight and criterion matrixes Table 7 shows the final calculated weights of each characteristic for the studied crop. The results showed that the slope was the most important factor in irrigated cultivation and had the highest coefficient and the lowest coefficient was the soil reaction. characteristics Crop Wheat textur e Humidit y Temprature ESP EC Table7- weights of land characteristics for wheat 4-Determining the land suitability class For determining the final suitability class in each land unit, characteristics matrix in each unit was combined in weight matrix and the final matrix obtained as it is seen in table 8. Table8- criterion matrix and different land units evaluation for wheat OC 0.26 Soil reaction Gypsum 0.05 CaCO Depth 0.11 Coarse particles 0.13 Slop Criterion matrix Evaluation matrix Land unit P [ S 2 S 3 N 1 N 2] E [ S 2 S 3 N 1 N 2] - P [ ] E [ ] 1 Elham Ghanbarie, et al. 348

7 P [ ] E [ ] 2 P [ ] E [ ] 3 P [ ] E [ ] 4 P [ ] E [ ] 5 P [ ] E [ ] 6 P [ ] E [ ] 7 P [ ] E [ ] 8 P [ ] E [ P [ ] E [ ] 10 As it is presented the theory of fuzzy collections not only makes clear the dominant land suitabilityclass, but determines the degree which belongs to other classes in considered land unit. These types of information are used in land management and sustainable agriculture. 5-Land indices and suitability classes by Fuzzy Set Theory Table 9 shows the calculated land indices by fuzzy set theory and table 10 shows the related suitability classes for wheat. Table 9- calculated land indices by fuzzy set theory for wheat in different land units Land Unit crop Wheat Table 10- different land unit suitability classes for wheat by fuzzy set theory Land unit crop wheat N N N Figure 10 shows the land suitability map of studied region for wheat by Fuzzy Set Theory Fig. 6- qualitative suitability classes for wheat by fuzzy method Regression relations between calculated land indices by fuzzy method and observed production are shown in figure 11. Obtained regression coefficient is significant (p>1%) that it shows the power of this method for the evaluation of the the land land under study for wheat. Elham Ghanbarie, et al. 349

8 Fig. 7-relations between calculated land indices by fuzzy method and observed production (Kg/ha) for wheat TOTAL CONCLUSION The accuracy of land suitability evaluation methods depends on the effect of the degree of selected land characteristics on crop production and the final suitability evaluation matrix in each land unit shows the suitability degree of lands related to different land suitability classes. Slope because of having the most weight is known as the most important criterion and humidity because of having the least weight is known as the least important criterion among the characteristics which are selected as effective properties in cultivating the wheat. The results of correlation between land indices and observed production in the studied region showed that although both of the methods are suitable, fuzzy set theory using similuation method (r2=0.91) has higher correlation than parametric methods and showed good results. There is a difference between parametric and fuzzy method correlations in wheat. This issue shows that sys tables for wheat should be modified according to the condition in Iran and they estimate the production less than the observed production. This study also shows that MATLAB software and selected transition ranges by researchers can estimate the land indices for wheat better than parametric methods. So it is suggested that fuzzy methods be used in making the land suitability maps to increase the accuracy of these maps to reach stable land managements. REFERENCES: 1. Anonymous, World reference base for soil resources (WRB), International soil classification system 2. Bakhtiarifar M, Mesgari M and Karimi M, Changing Land uses, using Spatial Multi-Criteria Decision Analyses. Intergrating Generations FIG Working Week June, Stockholm, Sweden. 3. Blanco-Canqui H and R Lal, Principles of Soil Conservation and Management. Springer.Blanco-Canqui, Humberto, Lal, XXIV. 4. Burrough PA, Fuzzy mathematical methods for soil survey and land evaluation. European Journal of Soil Science 40: Farajnia A, 2007 in Persian. Land suitability evaluation and sugarbeat production potential determination in Yekanat plain of Marand, volume 223, first number, pages for naming soils and creating legends for soil maps. World Soil Resources Reports No FAO, Rome. 6. Jafarzadeh AA, Alamdari P, Neyshabouri MR and Saedi S, Land suitability evaluation of Bilverdy Research Station for wheat, barley, alfalfa, maize and sunflower. Pp Bioclimatology and Natural Hazard, International Scientific Conferance September, Polana and Detvou, Slovakia. 7. Jiang P and Thelen KD, Effect of soil and topographic properties on crop yield in Elham Ghanbarie, et al. 350

9 North-central consoybean cropping system. Agronomy Journal 96: Khidir SM, A statistical approach in the use of parametric systems applied to the FAO framework for land evaluation. PhD Thesis of State University, Ghent, Belgium. 9. Malekian A, Jafarzadeh AA,2009 in Persian, quantitive classification of Khajeh research station lands for wheat. Barley, alfalfa and Safflower, Proceedings of the 11th International Iran Soil Congress, Gorgan. 10. Mohamadi J, 2007 in Persian. Pedometric 4 th volume, Fazzy set theory, pelk publivation,tehran. 11. Storie RE, Rating soils for agricultural forest and grazing use. Journal Transactions of Soil Science1: Sys C, Van Ranset E and Debaveye J, 1991a. Land Evaluation, Part I, Principle in Land Evaluation and Crop Production Calculation, International Training Center for Post Graduate Soil Scientists, Ghent Univercity, Ghent., Belgium. 13. Sys C, Van Ranset E and Debaveye J, 1991b. Land Evaluation, Part II, Methods in Land Evaluation. International Training Center for Post Graduate Soil Scientists, Ghent Univercity, Ghent, Belgium. 14. Sys C, Van Ranset E, Debaveye J and Beernaert F, Land Evaluation, Part III, Crop Requirements. General Administration for Development Cooperation Place, Brussels, Belgium. 15. Tang H, Debaveye J, Ruan D and Van Ranst E, Land suitability classification based on fuzzy set theory. Pedologie 3: Yasmina A, Moulay A, Najmia AM, Enrico B, Yasmina B, Paolo Omar C and Aldo D, Land evaluation in the province of Ben Slimane, Morocco. Pp Ministry of Foreign Affairs, Istituto Agronomico Per L'oltremare, Italy. Elham Ghanbarie, et al. 351

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