Assessment of soil quality parameters using multivariate analysis in the Rawal Lake watershed

• Published in: Environmental monitoring and assessment 2016-09, Vol.188 (9), p.533-533, Article 533
• Main Authors: Firdous, Shahana, Begum, Shaheen, Yasmin, Azra
• Format: Article
• Language: English
• Subjects: Aluminum Silicates - analysis; Anthropogenic factors; Atmospheric Protection/Air Quality Control/Air Pollution; Carbon; Cluster Analysis; Crop production; Earth and Environmental Science; Ecology; Ecosystem services; Ecotoxicology; Environment; Environmental Management; Environmental monitoring; Fertilizers; Lakes; Land use; Monitoring/Environmental Analysis; Multivariate Analysis; Nitrates; Nitrates - analysis; Nitrogen; Nutrient availability; Nutrients; Organic matter; Pakistan; Phosphates - analysis; Phosphorus; Principal Component Analysis; Principal components analysis; Productivity; Seasons; Soil - chemistry; Soil fertility; Soil organic matter; Soil pH; Soil quality; Soils; Sulfates - analysis; Sulfur; Temperature; Topography; Toxicity; Variance analysis; Water quality; Watershed management; Watersheds
• ISSN: 0167-6369; 1573-2959
• DOI: 10.1007/s10661-016-5527-5

Soil providing a wide array of ecosystem services is subjected to quality deterioration due to natural and anthropogenic factors. Most of the soils in Pakistan have poor status of available plant nutrients and cannot support optimum levels of crop productivity. The present study statistically analyzed ten soil quality parameters in five subwatersheds (Bari Imam, Chattar, Rumli, Shahdra, and Shahpur) of the Rawal Lake. Analysis of variance (ANOVA), cluster analysis (CA), and principal component analysis (PCA) were performed to evaluate correlation in soil quality parameters on spatiotemporal and vertical scales. Soil organic matter, electrical conductivity, nitrates, and sulfates were found to be lower than that required for good quality soil. Soil pH showed significant difference ( p  0.75) and indicated that these were the most influential parameters of first factor or component. Cluster analysis separated five sampling sites into three statistically significant clusters: I (Shahdra-Bari Imam), II (Chattar), and III (Shahpur-Rumli). Among the five sites, Shahdra was found to have good quality soil followed by Bari Imam. The present study illustrated the usefulness of multivariate statistical approaches for the analysis and interpretation of complex datasets to understand variations in soil quality for effective watershed management.