Multivariate analysis and GIS approaches for modeling and mapping soil quality and land suitability in arid zones

Assessment of Soil Spatial Variability in Agricultural Ecosystems Using Multivariate Analysis, Soil Quality Index (SQI), and Geostatistical Approach: A Case Study of the Mnasra Region, Gharb Plain, Morocco

“…Specifically, SQ denotes the soil's ability to adequately serve as a medium for plant growth (productivity), regulate water flow, act as a buffer, and effectively integrate its biological, physical, and chemical components [15]. A primary global objective today is the preservation of overall soil quality [16], which necessitates the identification of soils, determination of optimal management practices, and monitoring changes in soil properties [1]. Consequently, there has been a surge in research efforts aimed at identifying sensitive indicators and methods capable of detecting soil quality changes, thereby facilitating the assessment of soil hazards and management implications [17].…”

“…The SQI integrates pertinent soil indicator data into numerical values [18], typically reflecting regional variations in soil's chemical and physical attributes [19]. These attributes, encompassing physical, chemical, and biological characteristics, serve as key indicators of soil health and fertility status [16]. Physical indicators include soil depth, bulk density, porosity, aggregate stability, texture, and compaction, while chemical indicators encompass pH, salinity, organic matter content, nutrient availability, cation exchange capacity, nutrient cycling, and soil contaminant levels [20].…”

“…Multivariate statistical approaches (MSA), such as cluster analysis and principal component analysis (PCA), are commonly employed to discern the underlying mechanisms influencing soil quality [27,28]. These analytical tools greatly enhance data comprehension and facilitate informed decision-making across diverse fields, including soil science and environmental management [16,29]. Among these methods, PCA stands out as a prominent approach for reducing the dimensionality of data by identifying the most influential variables [30].…”

“…Among Kriging techniques, ordinary Kriging is deemed most suitable for producing accurately predicted distribution maps [35]. Previous research has extensively employed MSA in conjunction with GIS approaches to ascertain SQI [16]. Specifically, in Egypt, a study utilized GIS and MSA to evaluate and map SQI [28], focusing on the El-Fayoum depression in the Western Desert.…”