An assessment of pasture soils quality based on multi-indicator weighting approaches in semi-arid ecosystem


Karaca S., DENGİZ O., Turan I. D. , ÖZKAN B., DEDEOĞLU M., Gülser F., ...More

ECOLOGICAL INDICATORS, vol.121, 2021 (Peer-Reviewed Journal) identifier identifier

  • Publication Type: Article / Article
  • Volume: 121
  • Publication Date: 2021
  • Doi Number: 10.1016/j.ecolind.2020.107001
  • Journal Name: ECOLOGICAL INDICATORS
  • Journal Indexes: Science Citation Index Expanded, Scopus, Academic Search Premier, PASCAL, Aerospace Database, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, CAB Abstracts, Communication Abstracts, Compendex, Environment Index, Geobase, Index Islamicus, Metadex, Pollution Abstracts, Public Affairs Index, Veterinary Science Database, Civil Engineering Abstracts
  • Keywords: Soil quality, Pasture, Fuzzy-AHP, Principal component analysis, REOSAVI, Semi-arid ecosystem, MINIMUM DATA SET, VEGETATION INDEXES, FUZZY-AHP, CHLOROPHYLL CONTENT, MICROBIAL BIOMASS, CARBON, WHEAT, LAND, MANAGEMENT, NITROGEN

Abstract

The development of soil quality index in the vicinity of the Van Lake pasture lands located in the Northern East Part of Turkey under semi-arid terrestrial ecosystem is very important since there are certain degradation signs indicating how their sustainability is being threatened. A total of 150 soils in the pastures throughout the region were sampled and several soil physical, chemical and biological indicators were quantified. A minimum data set of the most sensitive indicators was chosen using principal component analyses. Linear scoring functions for these indicators were used to develop soil quality index integrated with remote sensing (RS) and geographical information system (GIS). In this current study, classes between SQIs calculated using the minimum data set (MDS) and total data set (TDS) approaches showed a parallel trend in each other and match analysis for agreement showed also a significant statistically relationship between TDSSQI/MDSSQI and REOSAVI in May and June months for pasture area. Furthermore, this study also showed that advance techniques (PCA, geostatistic, AHP-Fuzzy) and the technologies of RS and GIS, which are essential to the analysis and processing of original and generated information were used effectively by integrating each other for SQI in large area.