Quantitative Evaluation of Soil Quality Using Principal Component Analysis: The Case Study of El-Fayoum Depression Egypt

Mohamed K. Abdel-Fattah, Elsayed Said Mohamed, Enas M. Wagdi, Sahar A. Shahin, Ali A. Aldosari, Rosa Lasaponara and Manal A. Alnaimy
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Mohamed K. Abdel-Fattah: Soil Science Department, Faculty of Agriculture, Zagazig University, Zagazig 44519, Egypt
Elsayed Said Mohamed: National Authority for Remote Sensing and Space Sciences, Cairo 11843, Egypt
Enas M. Wagdi: Soil Science Department, Faculty of Agriculture, Zagazig University, Zagazig 44519, Egypt
Sahar A. Shahin: Soils and Water Use Department, Agriculture and Biological Division, National Research Center, Cairo 12622, Egypt
Ali A. Aldosari: Geography Department, King Saud University, Riyadh 11451, Saudi Arabia
Rosa Lasaponara: Italian National Research Council, C.da Santa Loja, Tito Scalo, 85050 Potenza, Italy
Manal A. Alnaimy: Soil Science Department, Faculty of Agriculture, Zagazig University, Zagazig 44519, Egypt

Sustainability, 2021, vol. 13, issue 4, 1-19

Abstract: Soil quality assessment is the first step towards precision farming and agricultural management. In the present study, a multivariate analysis and geographical information system (GIS) were used to assess and map a soil quality index (SQI) in El-Fayoum depression in the Western Desert of Egypt. For this purpose, a total of 36 geo-referenced representative soil samples (0–0.6 m) were collected and analyzed according to standardized protocols. Principal component analysis (PCA) was used to reduce the dataset into new variables, to avoid multi-collinearity, and to determine relative weights ( Wi ) and soil indicators (Si), which were used to obtain the soil quality index (SQI). The zones of soil quality were determined using principal component scores and cluster analysis of soil properties. A soil quality index map was generated using a geostatistical approach based on ordinary kriging (OK) interpolation. The results show that the soil data can be classified into three clusters: Cluster I represents about 13.89% of soil samples, Cluster II represents about 16.6% of samples, and Cluster III represents the rest of the soil data (69.44% of samples). In addition, the simulation results of cluster analysis using the Monte Carlo method show satisfactory results for all clusters. The SQI results reveal that the study area is classified into three zones: very good, good, and fair soil quality. The areas categorized as very good and good quality occupy about 14.48% and 50.77% of the total surface investigated, and fair soil quality (mainly due to salinity and low soil nutrients) constitutes about 34.75%. As a whole, the results indicate that the joint use of PCA and GIS allows for an accurate and effective assessment of the SQI.

Keywords: soil quality index; soil evaluation; geographic information; cluster analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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