Soil Science Department, Faculty of Agricultural Science, University of Lorestan, Lorestan, Iran
Abstract
Introduction:
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%. Conclusion:
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.
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Fariabi, A., & Matinfar, H. (2017). Soil quality assessment of a Semi-arid region using fuzzy logic and analytic hierarchy process technique: case study of Semnan Province's Telobin. Environmental Sciences, 15(3), 187-202.
MLA
Azar Fariabi; Hamidreza Matinfar. "Soil quality assessment of a Semi-arid region using fuzzy logic and analytic hierarchy process technique: case study of Semnan Province's Telobin", Environmental Sciences, 15, 3, 2017, 187-202.
HARVARD
Fariabi, A., Matinfar, H. (2017). 'Soil quality assessment of a Semi-arid region using fuzzy logic and analytic hierarchy process technique: case study of Semnan Province's Telobin', Environmental Sciences, 15(3), pp. 187-202.
VANCOUVER
Fariabi, A., Matinfar, H. Soil quality assessment of a Semi-arid region using fuzzy logic and analytic hierarchy process technique: case study of Semnan Province's Telobin. Environmental Sciences, 2017; 15(3): 187-202.