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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Mohamed K. Abdel-Fattah & Elsayed Said Mohamed & Enas M. Wagdi & Sahar A. Shahin & Ali A. Aldosari & Rosa Lasaponara & Manal A. Alnaimy, 2021. "Quantitative Evaluation of Soil Quality Using Principal Component Analysis: The Case Study of El-Fayoum Depression Egypt," Sustainability, MDPI, vol. 13(4), pages 1-19, February.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:4:p:1824-:d:495556
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    4. Inna Z. Kamanina & Wael M. Badawy & Svetlana P. Kaplina & Oleg A. Makarov & Sergey V. Mamikhin, 2023. "Assessment of Soil Potentially Toxic Metal Pollution in Kolchugino Town, Russia: Characteristics and Pollution," Land, MDPI, vol. 12(2), pages 1-16, February.
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    6. Elsayed A. Abdelsamie & Mostafa A. Abdellatif & Farag O. Hassan & Ahmed A. El Baroudy & Elsayed Said Mohamed & Dmitry E. Kucher & Mohamed S. Shokr, 2022. "Integration of RUSLE Model, Remote Sensing and GIS Techniques for Assessing Soil Erosion Hazards in Arid Zones," Agriculture, MDPI, vol. 13(1), pages 1-19, December.
    7. Elsayed M. Ramadan & Abir M. Badr & Fadi Abdelradi & Abdelazim Negm & Ahmed M. Nosair, 2023. "Detection of Groundwater Quality Changes in Minia Governorate, West Nile River," Sustainability, MDPI, vol. 15(5), pages 1-26, February.
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    9. Xianglong Fan & Pan Gao & Li Zuo & Long Duan & Hao Cang & Mengli Zhang & Qiang Zhang & Ze Zhang & Xin Lv & Lifu Zhang, 2023. "Soil Quality Evaluation for Cotton Fields in Arid Region Based on Graph Convolution Network," Land, MDPI, vol. 12(10), pages 1-18, October.
    10. Manal A. Alnaimy & Sahar A. Shahin & Ahmed A. Afifi & Ahmed A. Ewees & Natalia Junakova & Magdalena Balintova & Mohamed Abd Elaziz, 2022. "Spatio Prediction of Soil Capability Modeled with Modified RVFL Using Aptenodytes Forsteri Optimization and Digital Soil Assessment Technique," Sustainability, MDPI, vol. 14(22), pages 1-20, November.
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