Soil quality assessment of a Semi-arid region using fuzzy logic and analytic hierarchy process technique: case study of Semnan Province's Telobin

Document Type : Original Article


Soil Science Department, Faculty of Agricultural Science, University of Lorestan, Lorestan, Iran


Soil quality is considered to be one of the important indicators of sustainable agriculture and the environment. Based on sustainable agriculture goals and environmental protection, soil quality is defined the capacity of a specific kind of soil in  sustaining plant and animal productivity, maintaining or enhancing water and air quality, and supporting human health and habitation”. The main objective of this study is integrating AHP and fuzzy logic system to assess soil quality based on physical, chemical soil properties and their topographical characteristics.
Materials and Methods:
The study carried out in Telobin area located in northeast Shahrood County, Iran. The thermal regime of the study area is Mesic and its moisture regime is Xeric. Soil were sampled at 36 locations across study area describing all soil variability. Soil samples were analyzed for its physical and chemical soil properties and incorporated to topographical characteristics for further analysis. The map of each soil parameter and topographic index was created using the Inverse Distance Weighting Model. Thereafter, map of soil quality regarding physical, chemical and topographical indicators created by using integrated fuzzy and AHP approaches.  AHP Technique was used for weighting all above mentioned indicators.
Results and discussion:
In the term of soil quality the results show that, 3.01% was classified in high quality, 49.57% (2099.87 ha) was classified in poor quality, 44.33% (1877.33 ha) was classified in average quality and 3.5% (50/131 hectares) was classified in good quality. Soil quality was determined by using all indicators, but there are always a few important indicators with a higher weight as the key indicators. In this study soil depth index from physical indicators, organic carbon index from chemical index and slope index from topographic have higher weight. Therefor it was found that using hierarchical analysis-fuzzy logic method for the soil in studied area to determine the quality is well-established. Field observations of the region show that in areas with moderate soil quality, its use is forested and pasture. In areas with good soil quality, the amount of organic carbon and potassium is high and PH is in the range of 7-6, which the absorption of nutrients is high in this areas but in areas where the soil quality is poor or very poor, the amount of organic carbon is low or negligible and the slopes of the area are more than 30%.
The results of this study show that the organic carbon has the highest impact on the quality of soil in the studied area and, about the term of soil quality, most of the area has poor quality. Therefore, it can be argue that the use of the combination of fuzzy and AHP methods in GIS can categorize the status of soil quality to the quantitative levels in different groups. Using the fuzzy technique and opinion of experts can make a database for us. In general, the fuzzy logic approach is considered as a very suitable tool for modeling the physical, chemical, and topographic quality of the area that is considered as an input parameters.


  1. Abrishamchi, A., Tajrishy, M. and Marino, M., 2008. In Proceedings Awra Spring Specialty Conference.
  2. Alesheikh, A.A., Soltani, M.J., Nouri, N. and Khalilzadeh, M., 2008. Land assessment for flood spreading site selection using geospatial information system. International Journal of Environmental Science and Technology. 5, 455-462.
  3. Aparicio, V. and Costa, J.L., 2007. Soil quality indicators under continuous cropping systems in the Argentinean Pampas. Soil and Tillage Research. 96, 155-165.
  4. Arshad, M.A. and Martin, S., 2002. Identifying critical limits for soil quality indicators in agro-ecosystems. Agriculture, Ecosystems and Environment. 88, 153-160.
  5. Atalay, I., 2006. Toprak Oluşumu, Sınıflandırılması ve Coğrafyası. Çevre ve Orman Bakanlığı.
  6. Banaei, M.H., 1998. Map heat and moisture regimes in Iran.Research. Institute for Soil and Water Country. (In Persian with English abstract).
  7. Doran, J.W., Parkin, T.B. and Jones, A., 1996. Quantitative indicators of soil quality: A minimum data set. Methods for assessing soil quality. 25-37.
  8. El-saatty, T.L., 1980. The analytic hierarchy process: New York: McGraw-Hill New York.
  9. Islam K.R., Kamaluddin, M., Bhuiyan, M.K. and Badruddin, A., 1999. Comparative performance of exotic and indigenous forest species for tropical semi-evergreen degraded forest land reforestation in Bangladesh. Land Degrad. Dev. 10, 241-249.
  10. Karlen D. L. Ditzler C. A. and Andrews S. S., 2003. Soil quality: why and how? Geoderma. 114, 145-156.
  11. Kemmitt S.J., Wright, D. and Jones, D.L., 2005. Soil acidification used as a management strategy to reduce nitrate losses from agricultural land. Soil Biology and Biochemistry. 37, 867-875.
  12. Klute A., 1986. Methods of soil analysis. Part 1. Physical and mineralogical methods. American Society of Agronomy, Inc.
  13. Kremenova, O., 2004. Fuzzy modeling of soil maps. Pages 81. Helsinki University of technology department of surveying: Citeseer.
  14. Li, Q. and Yan, J., 2012. "Assessing the health of agricultural land with emergy analysis and fuzzy logic in the major grain-producing region." Catena 99: 9-17.
  15. Li, X. M. Min, M. and Tan, C.F., 2005. The functional assessment of agricultural ecosystems in Hubei Province, China. Ecological Modelling. 187, 352-360.
  16. Malczewski, J., 1999. GIS and multicriteria decision analysis. John Wiley & Sons. Inc. Pub., USA.
  17. Mosadeghi, R., Warnken, J., Tomlinson, R. and Mirfenderesk, H., 2015. Comparison of Fuzzy-AHP and AHP in a spatial multi-criteria decision making model for urban land-use planning. Computers, Environment and Urban Systems. 49, 54-65.
  18. Peche, R. and Rodriguez, E., 2012. "Development of environmental quality indexes based on fuzzy logic. A case study." Ecological Indicators. 23, 555-565.
  19. Qi, Y., Huang, B., Gu, Z., Sun, W. and Zhao, Y., 2008. Spatial and temporal variation of C/N ratio of agricultural soils in typical area of Yangtze Delta region and its environmental significance. B. Miner. Petro. Geochem. 27, 50-56.
  20. Schoeneberger, P.J., Wysocki, D.A., Benham, E.C. and Broderson, W.D., 2002. Field book for describing and sampling soils. Natural Resources Conservation Service, National Soil Survey Center,. Lincoln, NE.
  21. Soil Survey Staff. 2010. Keys to soil taxonomy NRCS, USDA, USA.
  22. Thapa, R.B. and Murayama, Y., 2008. Land evaluation for peri-urban agriculture using analytical hierarchical process and geographic information system techniques: A case study of Hanoi. Land Use Policy. 25, 225-239.
  23. Trangmar, B.B., Yost, R.S. and Uehara, G., 1985. Application of geostatistics to spatial studies of soil properties. Advances in agronomy. 38, 45-94.
  24. Wang, Z., Chang, A.C., Wu, L. and Crowley, D., 2003. Assessing the soil quality of long-term reclaimed wastewater-irrigated cropland. Geoderma. 114, 261-278.
  25. Ying, X., Zeng, G.M., Chen, G.Q., Tang, L., Wang, K.L. and Huang, D.Y., 2007. Combining AHP with GIS in synthetic evaluation of eco-environment quality—A case study of Hunan Province, China. Ecological Modelling. 209, 97-109.