4.7 Article

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

Journal

ECOLOGICAL INDICATORS
Volume 121, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.ecolind.2020.107001

Keywords

Soil quality; Pasture; Fuzzy-AHP; Principal component analysis; REOSAVI; Semi-arid ecosystem

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The development of soil quality index in the vicinity of the Van Lake pasture lands located in the Northern East Part of Turkey is crucial for assessing the sustainability of the semi-arid terrestrial ecosystem. By sampling 150 soils and quantifying various indicators, a soil quality index integrated with remote sensing and geographical information system was developed. The study found a parallel trend between the calculated SQIs using different data set approaches in the pasture area, with a significant statistical relationship between TDSSQI/MDSSQI and REOSAVI in May and June.
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.

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