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A GIS-Based Approach for the Quantitative Assessment of Soil Quality and Sustainable Agriculture

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  • Mostafa A. Abdellatif

    (National Authority for Remote Sensing and Space Science (NARSS), Cairo 11843, Egypt
    Soil and Water Department, Faculty of Agriculture, Tanta University, Tanta 31527, Egypt)

  • Ahmed A. El Baroudy

    (Soil and Water Department, Faculty of Agriculture, Tanta University, Tanta 31527, Egypt)

  • Muhammad Arshad

    (Department of Chemical Engineering, College of Engineering, King Khalid University, Abha 61321, Saudi Arabia)

  • Esawy K. Mahmoud

    (Soil and Water Department, Faculty of Agriculture, Tanta University, Tanta 31527, Egypt)

  • Ahmed M. Saleh

    (National Authority for Remote Sensing and Space Science (NARSS), Cairo 11843, Egypt)

  • Farahat S. Moghanm

    (Soil and Water Department, Faculty of Agriculture, Kafrelsheikh University, Kafr El-Sheikh 33516, Egypt)

  • Kamal H. Shaltout

    (Botany Department, Faculty of Science, Tanta University, Tanta 31527, Egypt)

  • Ebrahem M. Eid

    (Biology Department, College of Science, King Khalid University, Abha 61321, Saudi Arabia
    Botany Department, Faculty of Science, Kafrelsheikh University, Kafr El-Sheikh 33516, Egypt)

  • Mohamed S. Shokr

    (Soil and Water Department, Faculty of Agriculture, Tanta University, Tanta 31527, Egypt)

Abstract

Assessing soil quality is considered one the most important indicators to ensure planned and sustainable use of agricultural lands according to their potential. The current study was carried out to develop a spatial model for the assessment of soil quality, based on four main quality indices, Fertility Index (FI), Physical Index (PI), Chemical Index (CI), and Geomorphologic Index (GI), as well as the Geographic Information System (GIS) and remote sensing data (RS). In addition to the GI, the Normalized Difference Vegetation Index (NDVI) parameter were added to assess soil quality in the study area (western part of Matrouh Governorate, Egypt) as accurately as possible. The study area suffers from a lack of awareness of agriculture practices, and it depends on seasonal rain for cultivation. Thus, it is very important to assess soil quality to deliver valuable data to decision makers and regional governments to find the best ways to improve soil quality and overcome the food security problem. We integrated a Digital Elevation Model (DEM) with Sentinel-2 satellite images to extract landform units of the study area. Forty-eight soil profiles were created to represent identified geomorphic units of the investigated area. We used the model builder function and a geostatistical approach based on ordinary kriging interpolation to map the soil quality index of the study area and categorize it into different classes. The soil quality (SQ) of the study area, classified into four classes (i.e., high quality (SQ2), moderate quality (SQ3), low quality (SQ4), and very low quality (SQ5)), occupied 0.90%, 21.87%, 22.22%, and 49.23% of the total study area, respectively. In addition, 5.74% of the study area was classified as uncultivated area as a reference. The developed soil quality model (DSQM) shows substantial agreement (0.67) with the weighted additive model, according to kappa coefficient statics, and significantly correlated with land capability R 2 (0.71). Hence, the model provides a full overview of SQ in the study area and can easily be implemented in similar environments to identify soil quality challenges and fight the negative factors that influence SQ, in addition to achieving environmental sustainability.

Suggested Citation

  • Mostafa A. Abdellatif & Ahmed A. El Baroudy & Muhammad Arshad & Esawy K. Mahmoud & Ahmed M. Saleh & Farahat S. Moghanm & Kamal H. Shaltout & Ebrahem M. Eid & Mohamed S. Shokr, 2021. "A GIS-Based Approach for the Quantitative Assessment of Soil Quality and Sustainable Agriculture," Sustainability, MDPI, vol. 13(23), pages 1-24, December.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:23:p:13438-:d:695260
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Jie Wang & Li Li & Hang Yu, 2022. "Application of Domestic High-Resolution Satellite Data in Remote Sensing Geological Survey of the Metallogenic Belt in Zhejiang Province," Sustainability, MDPI, vol. 14(8), pages 1-20, April.

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