Abstract

Food security has become a global concern for humanity with rapid population growth, requiring a sustainable assessment of natural resources. Soil is one of the most important sources that can help to bridge the food demand gap to achieve food security if well assessed and managed. The aim of this study was to determine the soil quality index (SQI) for El Fayoum depression in the Western Egyptian Desert using spatial modeling for soil physical, chemical, and biological properties based on the MEDALUS methodology. For this purpose, a spatial model was developed to evaluate the soil quality of the El Fayoum depression in the Western Egyptian Desert. The integration between Digital Elevation Model (DEM) and Sentinel-2 satellite image was used to produce landforms and digital soil mapping for the study area. Results showed that the study area located under six classes of soil quality, e.g., very high-quality class represents an area of 387.12 km(2) (22.7%), high-quality class occupies 441.72 km(2) (25.87%), the moderate-quality class represents 208.57 km(2) (12.21%), slightly moderate-quality class represents 231.10 km(2) (13.5%), as well as, a low-quality class covering an area of 233 km(2) (13.60%), and very low-quality class occupies about 206 km(2) (12%). The Agricultural Land Evaluation System for arid and semi-arid regions (ALESarid) was used to estimate land capability. Land capability classes were non-agriculture class (C6), poor (C4), fair (C3), and good (C2) with an area 231.87 km(2) (13.50%), 291.94 km(2) (17%), 767.39 km(2) (44.94%), and 416.07 km(2) (24.4%), respectively. Land capability along with the normalized difference vegetation index (NDVI) used for validation of the proposed model of soil quality. The spatially-explicit soil quality index (SQI) shows a strong significant positive correlation with the land capability and a positive correlation with NDVI at R-2 0.86 (p < 0.001) and 0.18 (p < 0.05), respectively. In arid regions, the strategy outlined here can easily be re-applied in similar environments, allowing decision-makers and regional governments to use the quantitative results achieved to ensure sustainable development.

Keywords

spatial modeling; land capability; ALESarid; GIS; remote sensing; NDVI

Published in

Sustainability
2021, Volume: 13, number: 5, article number: 2893
Publisher: MDPI

SLU Authors

• Ahmed, Mukhtar

  • Department of Agricultural Research for Northern Sweden, Swedish University of Agricultural Sciences
    
  • Pir Mehr Ali Shah Arid Agriculture University

UKÄ Subject classification

Environmental Sciences


Publication identifier

DOI: https://doi.org/10.3390/su13052893

Permanent link to this page (URI)

https://res.slu.se/id/publ/111362