Modelling land degradation vulnerability (LDV) in the newly-reclaimed desert oases is a key factor for sustainable agricultural production. In the present work, a trial for usingremote sensing data, GIS tools, and Analytic Hierarchy Process (AHP) was conducted for modeling and evaluating LDV. The model was then applied within 144,566 ha in Farafra, an inland hyper-arid Western Desert Oases in Egypt. Data collected from climate conditions, geological maps, remote sensing imageries, field observations, and laboratory analyses were conducted and subjected to AHP to develop six indices. They included geology index (GI), topographic quality index (TQI), physical soil quality index (PSQI), chemical soil quality index (CSQI), wind erosion quality index (WEQI), and vegetation quality index (VQI). Weights derived from the AHP showed that the effective drivers of LDV in the studied area were as follows: CSQI (0.30) > PSQI (0.29) > VQI (0.17) > TQI (0.12) > GI (0.07) > WEQI (0.05). The LDV map indicated that nearly 85% of the total area was prone to moderate degradation risks, 11% was prone to high risks, while less than 1% was prone to low risks. The consistency ratio (CR) for all studied parameters and indices were less than 0.1, demonstrating the high accuracy of the AHP. The results of the cross-validation demonstrated that the performance of ordinary kriging models (spherical, exponential, and Gaussian) was suitable and reliable for predicting and mapping soil properties. Integrated use of remote sensing data, GIS, and AHP would provide an effective methodology for predicting LDV in desert oases, by which proper management strategies could be adopted to achieve sustainable food security.

The reduced availability of water resources in Egypt has imposed the need to intensify the use of wastewater for crop irrigation in the alluvial soils of anthropogenic origin. Relevant effects can derive from contents of potentially toxic metals (PTMs) in supply resources soils, crops, and groundwater in these areas. For this reason the PTM content has to be monitored to evaluate and minimize health hazards. Therefore, in this context, two areas of the SE Nile Delta subjected to 25 year of wastewater irrigation, using agricultural drainage water (ADW) and mixed wastewater (MWW) were chosen and compared with a nearby site irrigated with Nile freshwater (NFW). At each of the three sites, ten samples of irrigation water, topsoil, berseem clover (Trifolium alexandrinum L.) plants, and seven groundwater samples were collected and analyzed for Cr, Co, Cu, Pb, Ni, and Zn. Results indicate that the total contents of Co, Cu, Ni, and Zn in soils collected from the three sampling sites and Pb in the MWW-irrigated soils were higher than their average natural contents in the earth’s crust, indicating potential risks. The DTPA-extractable contents of Cu in the three sites, in addition to Pb and Zn in the MWW-irrigated soils, exceeded the safe limits. The MWW-irrigated soils showed a considerable degree of metal contamination, while the NFW- and ADW-irrigated soils showed moderate and low levels of contamination, respectively. The contents of the six PTMs in the three sites showed low individual ecological risks, except for Pb in the MWW-irrigated soils that showed a moderate risk; however, the overall ecological risk remained low in all samples. The values of Co, Cu, and Ni in berseem shoot in addition to Pb from the MWW-irrigated soils were over the maximum permissible levels for animal feeding. Values of root-to-shoot translocation factor were lower than 1.0 for Cr, Co and Ni but higher than 1.0 for Cu, Pb, and Zn. Berssem plant is a good candidate for phytofiltration of Cr, Co and Ni, while for extracting Cu, Pb and Zn from polluted soils. The groundwater samples collected from the three sampling sites showed lower metal concentrations than the safe limits for drinking standards. Further remediation studies should be taken into account to alleviate potential environmental and health-related risks when using supply resources different from freshwater.

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A precise evaluation of soil quality (SQ) is important for sustainable land use planning. This study was conducted to assess soil quality using multivariate approaches. An assessment of SQ was carried out in an area of Dakhla Oasis using two methods of indicator selection, i.e., total data set (TDS) and minimum data set (MDS), and three soil quality indices (SQIs), i.e., additive quality index (AQI), weighted quality index (WQI), and Nemoro quality index (NQI). Fifty-five soil profiles were dug and samples were collected and analyzed. A total of 16 soil physicochemical parameters were selected for their sensitivity in SQ appraising to represent the TDS. The principal component analysis (PCA) was employed to establish the MDS. Statistical analyses were performed to test the accuracy and validation of each model, as well as to understand the relationship between the used methods and indices. The results of principal component analysis (PCA) showed that soil depth, gravel content, sand fraction, and exchangeable sodium percentage (ESP) were included in the MDS. High positive correlations (r ≥ 0.9) occurred between SQIs calculated using TDS and/or MDS under the three models. Moreover, the findings showed highly significant differences (p < 0.001) among SQIs within and between TDS and MDS. Approximately 80 to 85% of the total study area based on TDS, as well as 70 to 75%, according to MDS, were identified as suitable soils with slight limitations on soil quality grade (Q3, Q2, and Q1), while the remaining 20 to 30% had high to severe limitations (Q4 and Q5). The highest sensitivity (SI = 2.9) occurred by applying WQI using MDS and indicator weights based on the variance of PCA. Furthermore, the highest linear regression value (R2 = 0.88) between TDS and MDS was recorded using the same model. Because of its high sensitivity, such a model could be used for monitoring SQ changes caused by agricultural practices and environmental factors. The findings of this study have significant guiding implications and practical value in assessing the soil quality using TDS and MDS in arid areas critically and accurately.

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