Today, the global food security is one of the most pressing issues for humanity, and, according to Food and Agriculture Organisation (FAO), the increasing demand for food is likely to grow by 70% until 2050. In this current condition and future scenario, the agricultural production is a critical factor for global food security and for facing the food security challenge, with specific reference to many African countries, where a large quantities of rice are imported from other continents. According to FAO, to face the Africa’s inability to reach self-sufficiency in rice, it is urgent “to redress to stem the trend of over-reliance on imports and to satisfy the increasing demand for rice in areas where the potential of local production resources is exploited at very low levels” The present study was undertaken to design a new method for land evaluation based on soil quality indicators and remote sensing data, to assess and map soil suitability for rice crop. Results from the investigations, performed in some areas in the northern part of the Nile Delta, were compared with the most common approaches, two parametric (the square root, Storie methods) and two qualitative (ALES and MicrioLEIS) methods. From the qualitative point of view, the results showed that: (i) all the models provided partly similar outputs related to the soil quality assessments, so that the distinction using the crop productivity played an important role, and (ii) outputs from the soil suitability models were consistent with both the satellite Sentinel-2 Normalize Difference Vegetation Indices (NDVI) during the crop growth and the yield production. From the quantitative point of view, the comparison of the results from the diverse approaches well fit each other, and the model, herein proposed, provided the highest performance. As a whole, a significant increasing in R² values was provided by the model herein proposed, with R² equal to 0.92, followed by MicroLES, Storie, ALES and Root as R² with value equal to 0.87, 0.86, 0.84 and 0.84, respectively, with increasing percentage in R² equal to 5%, 6% and 8%, respectively. Furthermore, the proposed model illustrated that around (i) 44.44% of the total soils of the study area are highly suitable, (ii) 44% are moderately suitable, and (iii) approximately 11.56% are unsuitable for rice due to their adverse physical and chemical soil properties. The approach herein presented can be promptly re-applied in arid region and the quantitative results obtained can be used by decision makers and regional governments.

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 km² (22.7%), high-quality class occupies 441.72 km² (25.87%), the moderate-quality class represents 208.57 km² (12.21%), slightly moderate-quality class represents 231.10 km² (13.5%), as well as, a low-quality class covering an area of 233 km² (13.60%), and very low-quality class occupies about 206 km² (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 km² (13.50%), 291.94 km² (17%), 767.39 km² (44.94%), and 416.07 km² (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 R² 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.

Recent decades have witnessed a danger to food security as well as to human health because of pollutants’ negative impact on crop quality. An accurate estimate of the heavy metal concentrations in Egypt’s north Nile Delta is required to lower the high concentration levels of heavy metal in the soil as a means to develop a remediation strategy that stabilizes heavy metals in contaminated soil. Using a geo-accumulation index (I-geo), contamination factor (CF), Improved Nemerow’s Pollution Index (Pn), and Potential Ecological Risk Index (PERI), supported by GIS; principal component analysis (PCA), and cluster analysis, six heavy metals (As, Co, Cu, Ni, V, and Zn) were analyzed from 15 soil profile layers (61 soil samples) to determine the extent of the soil contamination in the area studied. The findings demonstrate the widespread I-geo contamination of As, Co, Cu, Ni, V, and Zn in different layers. The ranges for the I-geo values were from −8.2 to 5.3; 4.11 to 1.8; 6.4 to 1.9; −9.7 to 2.8; −6.3 to 2.9; and from −12.5 to 2.4 for As, Co, Cu, Ni, V, and Zn, respectively. I-geo categorization therefore ranged from uncontaminated to strongly/extremely contaminated. The CF values varied from 0.01 to 60.6; 0.09 to 5.17; 0.02 to 10.51; 0 to 10.51; 0.02 to 7.12; and 0 to 7.68 for As, Co, Cu, Ni, V, and Zn, respectively. In decreasing sequence, the CFs are arranged as follows: CF (As), CF (Ni), CF (Zn), CF (V), CF (Cu), and CF (Co). Most of the research region (71.9%) consisted of a class of moderately to heavily polluted areas. Additionally, a large portion of the study region (49.17%) has a very high risk of contamination, as per the results of the PERI index. The use of a correlation matrix, cluster analysis, and principal component analysis(PCA) to evaluate the variability in the soil’s chemical content revealed the impact from anthropogenic activities on the heavy metal concentration levels in the study area’s soil. The current findings reflect the poor quality of management in the research region, which led to the increase in the concentration of heavy metals in the soil. Decision-makers could use the outcomes from the spatial distribution maps for contaminants and their levels as a basis for creating heavy metal mitigation strategies.

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 R² (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.

Land evaluation has an important role in agriculture. Developing countries such as Egypt face many challenges as far as food security is concerned due to the increasing rates of population growth and the limited agriculture resources. The present study used multivariate analysis (PCA and cluster analysis) to assess soil capability in drylands, Meanwhile the Almagra model of Micro LEIS was used to evaluate land suitability for cultivated crops in the investigated area under the current (CS) and optimal scenario (OS) of soil management with the aim of determining the most appropriate land use based on physiographic units. A total of 15 soil profiles were selected to characterize the physiographic units of the investigated area. The results reveal that the high capability cluster (C1) occupied 31.83% of the total study area, while the moderately high capability (C2), moderate capability (C3), and low capability (C4) clusters accounted for 37.88%, 28.27%, and 2.02%, respectively. The limitation factors in the studied area were the high contents of CaCO₃, the shallow soil depth, and the high salinity and high percentage of exchangeable sodium (% ESP) in certain areas. The application of OS enhanced the moderate suitability (S3) and unsuitable clusters (S5) to the suitable (S2) and marginally suitable (S4) categories, respectively, while the high suitability cluster (S1) had increased land area, which significantly affected the suitability of maize crop. The use of multivariate analysis for mapping and modeling soil suitability and capability can potentially help decision-makers to improve agricultural management practices and demonstrates the importance of appropriate management to achieving agricultural sustainability under intensive land use in drylands.