Using fuzzy-AHP and parametric technique to assess soil fertility status in Northeast of Iran

Keshavarzi A., Tuffour H. O., Bagherzadeh A., Tattrah L. P., Kumar V., Gholizadeh A., ...More

JOURNAL OF MOUNTAIN SCIENCE, vol.17, no.4, pp.931-948, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 17 Issue: 4
  • Publication Date: 2020
  • Doi Number: 10.1007/s11629-019-5666-6
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Agricultural & Environmental Science Database, Aquatic Science & Fisheries Abstracts (ASFA), CAB Abstracts, Environment Index, Geobase, Pollution Abstracts, Veterinary Science Database
  • Page Numbers: pp.931-948
  • Hacettepe University Affiliated: No


A good understanding of the levels and distribution patterns of soil properties and/or quality indicators is a prerequisite for developing sustainable agricultural land management programs. Traditional assessments of these parameters of soil fertility status are somewhat costly, in both economics and time aspects. Different modelling techniques have been proposed as a useful tool for determination of soil quality indicators and development of soil fertility maps, but to what extent these results are reliable remains under-quantified in many regions worldwide. To address this uncertainty, Fuzzy-Analytical Hierarchy Process (Fuzzy-AHP) and Parametric analyses were conducted to ascertain the soil fertility status of a semiarid region in the Northeast of Iran for some selected crops: alfalfa, corn silage, potato, sugar beet, tomato and wheat. The Fuzzy-AHP and Parametric techniques using soil suitability indices were estimated for each crop and each soil delineation was achieved by Ordinary Kriging. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was used as a compensatory method to allow tradeoffs among the selected criteria. Our results demonstrated that from the Fuzzy-AHP analysis, the soil fertility indices ranged from moderate to high for production of alfalfa; from low to high for production of corn silage and sugar beet; moderate to high for production of potato and tomato; and from low to moderate for production of wheat. However, the parametric analysis showed soil fertility classes ranging from very low to high for production of all the selected crops. High correlations were also observed between soil fertility indices predicted by both models. Similarly, the capacities of both models to predict soil fertility status for production of the selected crops were also highly correlated. The preference for the cultivation of the selected crops based on the Fuzzy-AHP analysis was sugar beet > corn silage > wheat > alfalfa > tomato > potato. On the other hand, using Parametric techniques, the crops preferences for cultivation ranked as corn silage > wheat > alfalfa > sugar beet > tomato > potato. We concluded that the findings would help to develop sustainable plans of cultivation based on patterns related to soil fertility classes depending on each crop's requirement.