Document Type : Research Article

Author

Assistant Professor of Soil and Water Research Institute (SWRI), Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran

Abstract

Introduction: In land suitability evaluation using parametric method, Khiddir or square root method (LQSI) and/or Storie method (LSI) are employed to calculate land index (LI), then suitability classes could be determined based on the LI. However, the obtained LI should be corrected according to the minimum rating (Rmin) and then the suitability classes should be determined. The existing functions to correct the LI should be mathematically continuous at all points in order to prevent from losing some LIs and their consequent suitability classes. In the functions represented by Sys, there is a continuity for S1 (suitable), S2 (moderately suitable) and S3 (marginal suitable) classes, but for N (unsuitable) the presented functions are not continuous. Therefore, presented functions for N1 and N2 classes can be very misleading since they are not able to distinguish between N1 and N2 classes and have problem to calculate them.
Materials and Methods: In this study, the existing functions in the literature were mathematically evaluated for each land suitability classes. Properties and criteria for determining land suitability classes are shown in Table1.  In parametric approach, land index (uncorrected land index) is calculated using Kiddir and Storrie methods as shown in equations 1and 2, respectively. The relationships between uncorrected land indices and corrected land indices are presented in Table 2.

(1)
(2)

According to continuity rules, the necessary corrections were made for N1 and N2 classes. Then numerical simulation was employed to assess the obtained results from the both existing and purposed functions and compared them with one another. For this purpose, one million random values were created for each of the S1 to N2 classes; so that the minimum rating (Rmin) was a random number for each class in own defined range and the other seven characteristics were random numbers between Rmin and 100. For example, in the S3 class, a minimum random number is in the range of 40 to 60 and seven other characteristics were between the Rmin and 100. Finally, a total of two million random simulations were created.
Results and Discussion: Based on the minimum, maximum and mean obtained values the simulation process is acceptable. These numbers show that the simulations have simulated almost all the cases that may occur in reality, from the best to the worst. The results showed that for N1 and N2 classes the correction functions should be respectively 12.5 + 0.314LQSI and 0.5LQSI for the Khiddir method and 12.5+ 0.313LSI and 0.5LSI for the Storie method to maintain the both the continuity of the correction functions for all classes and the corrected land index to be in the defined range for each class. The two million times simulation results also confirmed the accuracy of the obtained functions Therefore, it is suggested to use the proposed functions in determining N1 and N2 classes instead of Sys’s functions.
Conclusion: The use of the usual land index, which is conventionally calculated by the Khiddir or Storie method, called uncorrected land index (UCLI), can be largely misleading without being corrected and converted to the corrected land index (CLI), causing the wrong land suitability classes. Therefore, it is very important to use the relationships that have been developed for this purpose to correct the usual land index. The findings of this study showed that the current functions, although at the order level can distinguish between unsuitable order (N) from the S3 class, but separation between classes N1 and N2 are very difficult to calculate. For this reason, new relationships for N1 and N2 classes were calculated and presented. Therefore, it is suggested that N1 and N2 classes can be used instead of the relationships presented.

Keywords

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1- Amini fasakhodi, A. Bagheri, M., Salehi, M., Hadinezhad, A. 2014. Improvement of Resources Management and Quality of Land Suitability Evaluation Maps Using Fuzzy Approach (Case Study: Farrokhshahr - Chaharmahal & Bakhtiari). Geography and Environmental Planning, 24(4), 195-204. (in Persian with English abstract)
2- Alikhah-asl M and Naseri D. 2018. Ecological land capability evaluation for agriculture and range management using Fuzzy AHP method (Case study: Ghoorichay catchment, Ardabil province). Journal of Environmental Science and Technology (JEST). Online. (in Persian with English abstract)
3- Bagheri, M., Jelokhani Noaryki, M., Bagheri, K. 2018. Investigation of the land potential of Kermanshah province for rainfed wheat cultivation using artificial neural network. Journal of RS and GIS for Natural Resources, 8(4), 36-48. (in Persian with English abstract)
4- Bagheri, M. 2011. Applied Land Evaluation and Land Use Planning. Pelk Publications. 392 p. (in Persian)
5- Bagheri Bodaghabadi, M., Amini A., M.H. Salehi, Hosseinifard J and Heydari M. 2019. Suitability analysis and evaluation of pistachio orchard farming, using canonical multivariate analysis. Scientia Horticulturae. 246: 528-534.
6- Bagheri Bodaghabadi, M., Jose A. Martinez-Casasnovas, P., Khalili and M. Masihabadi, 2015. Assessment of the FAO traditional land evaluation methods, A case study: Iranian Land Classification method. Soil and Use Management, 31: 384-396.
7- Baroudy A.A., 2016.Mapping andevaluating land suitability usingaGIS-basedmodel. Catena 140, 96–104.
8- Delsouz Khaki, B., Honarjoo N., Davatgar N., Jalalian A., Torabi Gol sefidi H. 2018. Land Suitability Evaluation and Inherent Soil Fertility Quality for Rice Cultivation in Paddy Fields of Shaft and Fouman Counties. Iranian Journal of Soil Research, 32(1), 115-127. doi: 10.22092/ijsr.2018.116566. (in Persian with English abstract)
9- FAO. 1979. Soil Survey Investigations for Irrigation. FAO Soils Bulletin No. 42, Rome. 188p
10- Habibie, M.I., Noguchi, R., Shusuke, M. and Ahamed T. 2019. Land suitability analysis for maize production in Indonesia using satellite remote sensing and GIS-based multicriteria decision support system. GeoJournal (2019). https://doi.org/10.1007/s10708-019-10091-5
11- Keshavarzi, A; F. Sarmadian,; A. Heidari, and M. Omid. 2010. Land Suitability Evaluation Using Fuzzy Continuous Classification (A Case Study: Ziaran Region). Modern Applied Science, 4( 7). 72-81.
12- Khiddir. S.M. 1986. A statistical approach in the use of parametric systems applied to the FAO framework for land evaluation. Unpublished thesis. State University Ghent.
13- Kim H, Shim K. 2018. Land suitability assessment for apple (Malus domestica) in the Republic of Korea using integrated soil and climate information, MLCM, and AHP. Int J Agric & Biol Eng, 11(2): 139–144.
14- Mahdavi M., Esfandiarpoor I. and Bagheri M. 2016. Comparison of two fuzzy methods to determine the optimum soil depth in land suitability evaluation for wheat. Soil management and sustainable production 6 (3): 101-116.
15- Moravej K, Delavar M, Najafi V. 2018. Importance of Using Modern Irrigation Methods in Increase of Employment and Development of Rural Areas. geores. 33 (2) :175-190. (in Persian with English abstract)
16- Mosleh Z., Salehi M., Jafari A., Mehnatkesh A., and Esfandiarpoor Borujeni I . 2018. Assessing the Performance of Digital Mapping Approaches for the Qualitative Land Suitability Evaluation (A Case Study: Shahrekord Plain, Chaharmahal-Va-Bakhtiari Province). Journal Of Water and Soil, 32(1), 87-99. (in Persian with English abstract)
17- Mosleh, Z., Salehi, M. Hassan, Amini Fasakhodi, A., Jafari, A., Mehnatkesh, A., & Esfandiarpoor Borujeni, I. 2017. Sustainable allocation of agricultural lands and water resources using suitability analysis and mathematical multi-objective programming. Geoderma, 303, 52-59. doi: 10.1016/j.geoderma.2017.05.015
18- Movahedi Naeini, S. 1993. Evaluation of land suitability of important agricultural products in Gorgan region. Faculty of Agriculture, Tarbiat Modares University, 217 p. (in Persian with English abstract)
19- RahmatiF., & KamaliA. (2016). Comparison of Four Weighting Methods in Fuzzy-based Land Suitability to Predict Wheat Yield. Journal of Water and Soil, 31(1), 277-285.
20- Servati M. 2018. ELECTRE Tri Method Performance on Land Suitability Evaluation in Chalderan Region for Potato. Journal of Water and Soil Conservation, 25(1), 271-284. (in Persian with English abstract)
21- Servati M., Momtaz H., Zali Vargahan B., Mohammadi H. 2016. Performance evaluation of corrected land indices to determine the Potential of Maize production using FAO Method. Applied Soil Research, 3(1), 65-77. (in Persian with English abstract)
22- Seyed Jalali, S., Sarmadian, F., Shorafa, M. 2014. Comparison of Corrected and Uncorrected Land Indices in Parametric Method of Land Suitability Evaluation. Iranian Journal of Soil Research, 28(1), 127-141. doi: 10.22092/ijsr.2014.120157. (in Persian with English abstract)
23- Seyedmohammadi, J., Sarmadian, F., Jafarzadeh, A. and Ghorbani, M.A. 2018. Application of SAW, TOPSIS and fuzzy TOPSIS models in cultivation priority planning for maize, rapeseed and soybean crops Geoderma 310, 178-190.
24- Storie R.E. 1978. The Storie Index Soil Rating Revised. Davis,CA, University of California, Division of Agricultural Science, Special Publication No 3203.
25- Sys, C., E. Van Ranst, and J. Debaveye. 1991. Land evaluation, Part II. Methods in Land Evaluation. International Training center for post graduate soil scientists, Ghent University, Ghent. 247 pp.
26- Vasu, D.; Srivastava, R.; Patil, N.G.; Tiwary, P.; Chandran, P.; Kumar Singh, S. 2018. A comparative assessment of land suitability evaluation methods for agricultural land use planning at village level. Land Use Policy 2018, 79, 146–163.
27- Yohannes H. and Soromessa T. 2018. Land suitability assessment for major crops by using GIS-based multi-criteria approach in Andit Tid watershed, Ethiopia. Cogent Food & Agriculture, 4: 1470481
28- Zeinadini Meymand A., Bagheri Bodaghabadi, M., Moghimi A., Navidi N., Ebrahimi Meymand F. and Amirpou M. 2018. Modeling of yield and rating of land characteristics for corn based on artificial neural network and regression models in south of Iran. Desert 23(1): 85-95.