Assessment of soil quality in arid zones using principal component analysis and GIS-based modeling - Mohamed S. Shokr; Mohamed E. M. Jalhoum; Ahmed S. A. Sayed; Mohammed Saeed; Nazih Y. Rebouh; Elsayed Said Mohamed; Ibraheem A. H. Yousif; Hend H. Abdelhameed (PLoS ONE, 2025)
Background Assessing the quality of the soil is a crucial first step in agricultural management. A major obstacle to raising agricultural output, economic growth, and environmental health has been the decline in soil quality. One of the most often used metrics for evaluating soil quality is the soil quality index (SQI), which is frequently calculated using principal component analysis (PCA). Methodology In this study, a soil quality index in the southwest of the Ismailia Governorate, Egypt, was evaluated and mapped using a geographical information system (GIS) and multivariate analysis. (PCA). Fifty- one soil samples were gathered for this purpose, and they were examined using established procedures. The dataset was broken down into new variables using principal component analysis (PCA) to avoid multi-collinearity. Relative weights (Wi) and soil indicators (Si) were then established and used to calculate SQI. The SQI comprises three quality zones. Results and discussion The first zone has a very good quality index, accounting for about 65.66 (ha) of the entire area. Soils in this zone were defined by low salinity of the groundwater and adequate values of each soil attribute. The second zone, which makes up about 414.76 ha (67.5%) of the total area, is characterized by its good-quality soil. About 133.91 ha (21.8%) of the total land area is in the third zone, which is fair (bad quality). Low concentrations of soil organic matter (SOM), salinity, accessible nitrogen (N), phosphorus (P), potassium (K), and cation exchange capacity (CEC) had the greatest effects on the SQI of the studied location. Combining PCA and GIS enables a precise and efficient evaluation of the SQI. Conclusion Decision-makers can identify regions with very good, good, and poor soil quality by examining the generated spatial distribution maps. Additionally, they can learn how each characteristic influences plant growth. In addition, The methodology outlined in this work can be readily replicated in similar situations in arid regions, enabling local authorities and decision-makers to make use of the quantitative results achieved to guarantee long-term development.