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.

There is consensus on the impact of wastewater irrigation on soil properties and heavy metal accumulation. The studies that show the impact of temporal changes as a result of different long-term additions of wastewater on the heavy metal accumulation and degradation of soil are extremely limited. This study was carried out to assess heavy metal contamination in soils irrigated with wastewater for more than 30 years in Egypt. A total number of 12 irrigation water samples and 12 soil profiles were collected during 2020 and were chemically characterized. The results showed that soils irrigated with wastewater over the long term contained significantly higher concentrations of heavy metals compared to fields irrigated with fresh water. Heavy metal levels in water and soil samples were within the permissible limits, with the exception of Cd concentration in water (0.03 mg L⁻¹). Continuous cultivation for a long period of time (30 years) using raw urban wastewater application has led to the adverse effect of increasingly available Pb concentration (5.44 mg kg⁻¹). Similar temporal behavior was seen for Cd and Fe, which increased by 0.98 and 11.2 mg kg⁻¹, respectively, after 30 years. The heavy metals in wastewater-irrigated soils significantly increased in clayey soils, as compared to sandy soils irrigated from the same source. Our findings provide important information for decision makers in Egypt and similar countries for the development of a strategy for the use of wastewater in irrigation for sustainable agricultural management.

To meet the needs of Egypt’s rising population, more land must be cultivated. Land evaluation is vital to achieving sustainable agricultural production. To determine the soil capability in the northeast Nile Delta region of Egypt, the present study introduces a new form of integration between the Agriculture Land Evaluation System (ALES Arid) model and the machine learning (ML) approach. The soil capability indicators required for the ALES Arid model were determined for the 47 collected soil profiles covering the study area. These indicators include soil pH, soil salinity, the sodium adsorption ratio (SAR), the exchangeable sodium percentage (ESP), the organic matter (OM) content, the calcium carbonate (CaCO₃) content, the gypsum content, the clay percentage, and the slope. The ALES Arid model was run using these indicators, and soil capability indexes were obtained. Using GIS, these indexes helped to classify the study area into four capability classes, ranging from good to very poor soils. To predict the soil capability, three machine learning algorithms named traditional RVFL, sine cosine algorithm (SCA), and AFO were also applied to the same soil criteria. The developed ML method aims to enhance the prediction of soil capability. This method depends on improving the performance of Random Vector Functional Link (RVFL) using an optimization technique named Aptenodytes Forsteri Optimization (AFO). The operators of AFO were used to determine the best parameters of RVFL since traditional RVFL is sensitive to parameters. To assess the performance of the developed AFO-RVFL method, a set of real collected data was used. The experimental results illustrate the high efficacy of AFO-RVFL in the spatial prediction of soil capability. The correlations found in this study are critical for understanding the overall techniques for predicting soil capability.

Sustainable development goals (SDGs) 2 (zero hunger), 6 (clean water and sanitation), and 15 (life on land) are related to the human-water-soil nexus. Soil organic carbon and nutrients can be removed and transported to waterways through runoff and drainage. The main goals of this study are to quantify the water quality for irrigation and assess the dissolved organic carbon (DOC) contents in streams in the northern Nile Delta, Egypt. A 4-year water quality monitoring program is accomplished by collecting 35 irrigation and drainage water samples per year from the study area. The measured water quality parameters are as follows: salinity, pH, Na, SAR, Cl, and NO₃–N. In addition, the DOC content is accessed. The salinity hazard ranged from moderate, for most irrigation samples, to high and very high for drainage samples. All collected water samples have low to medium sodium hazards. Results indicate that average DOC contents in irrigation canals are 2.32 and 2.93 mg L⁻¹ for the summer and winter, respectively. The respective means of DOC concentration in drainage canals for the summer and winter seasons are around 3.96 and 5.09 mg L⁻¹. This study revealed significant differences in EC, pH, Na, Cl, and SAR, as water quality parameters, between irrigation and drainage canals. Additionally, the studied agroecosystem has seasonal variability in DOC concentration in irrigation and drainage canals between summer and winter. Overall, reusing drainage water for irrigation in the study area requires the selection of suitable crops and site-specific management.

El-Tina plain comprises an expansive landscape of soils that are collectively covered by the mega soil reclamation project of El-Salam/Sheikh Gaber Canal. The objective of this work is to prepare a database for some localities in the area, a prerequisite to allocative efficiency nexus for sustainable development in Egypt. A reconnaissance survey led to choosing 16 sites, each was completely described in the field and sampled for subsequent analysis. Field inspection revealed that the soils are barren with shallow watertable. Soil genesis indicates that they are derived from the defunct Pelusiac Branch of the Nile that used to run across northwestern Sinai. Particle size analysis revealed that some soils contain up to 80 % clay. Chemical analysis revealed that most soils are heavily infested with salinity and sodicity, aside from other constraints including salt crusts. Due to salinity perturbation, exchangeable sodium percentage (ESP) is not correlated with sodium adsorption ratio (SAR). The dominant soluble cation is Na+ at 1323.34 cmole l-1 followed in sequence by Mg2+ at 867.59 cmole l-1, Ca2+ at 386.44 cmole l-1, and K at 57.85 cmole l-1. The dominant soluble anion is Cl- at 1414.41 cmole l-1 followed by SO42- at 1193.90 cmole l-1, whereas the HCO3- is below one cmolel-1.The average EMgP stands at 48.85 compared with ESP at 31.75. This is confirmative evidence indicating seawater intrusion. Given these provisions, it is concluded that soil reclamation in the investigated localities for crop cultivation is dubious. An aquaculture production system may turn out to be a sagely alternative scenario.