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The issue of agricultural soil pollution is especially important as it directly affects the quality of food and the lives of humans and animals. Soil pollution is linked to human activities and agricultural practices. The main objective of this study is to assess and predict soil contamination by heavy metals utilizing an innovative method based on the adaptive neuro-fuzzy inference system (ANFIS), an effective artificial intelligence technology, and GIS in a semiarid and dry environment. A total of 150 soil samples were randomly collected in the neighboring area of the Bahr El-Baqar drain. Ordinary kriging (OK) was employed to generate spatial pattern maps for the following heavy metals: chromium (Cr), iron (Fe), cadmium (Cd), and nickel (Ni). The adaptive neuro-fuzzy inference system (ANFIS), known as one of the most effective applications of artificial intelligence (AI), was utilized to predict soil contamination by the selected heavy metals (Cr, Fe, Cd, and Ni). In total 150 samples were used, 136 soil samples were used for training and 14 for testing. The ANFIS predicting results were compared with the experimental results; this comparison proved its effectiveness, as a root mean square error (RMSE) was 0.048594 in training, and 0.0687 in testing, which is an acceptable result. The results showed that both the exponential and spherical models were quite suitable for Cr, Fe, and Ni. The correlation values (R2) were close to one in training and test; however, the stable model performed well with Cd. The high concentration of heavy metals was the most prevalent, encompassing approximately 51.6% of the study area. Furthermore, the average concentration of heavy metals in this degree was 82.86 ± 15.59 mg kg−1 for Cr, 20,963.84 ± 4447.83 mg kg−1 for Fe, 1.46 ± 0.42 mg kg−1 for Cd, and 48.71 ± 11.88 mg kg−1 for Ni. The comparison clearly demonstrates that utilizing the ANFIS model is a superior option for predicting the level of soil pollution. Ultimately, these findings can serve as a foundation for decision-makers to develop acceptable measures for mitigating heavy metal contamination. [ABSTRACT FROM AUTHOR]

Globally, Food security is considered one of the most issues for humanity due to rapid population growth thus sustainable natural resources assessment is required. Well assessment and management of soil can aid in achieving food security. Agriculture sector in Egypt is facing some obstacles related to sustainability. These include scarce land and water resources, degradation of environment and high rate of population growth. This study focuses on evaluating of agricultural sustainability development in Bahariya oasis, western desert of Egypt. Maps of physiographic and soils were produced using analysis of multispectral Sentinal-2 image with spatial resolution 10 m dropped over digital elevation model (DEM), A shuttle radar topography mission (SRTM) 1-arc-second v.30 DEM. Fifty soil profiles were dug to represent geomorphological units within study area. Soil productivity, environmental security, environmental protection, economic viability, and social acceptability of proposed management options were calculated within the study's landscapes using the Framework for Evaluating Sustainable Land Management (FESLM). The results revealed that the investigated area classified into lands that are marginally below the requirement of sustainability with an area of 534.34 km2 and the rest of study area are not meet sustainability requirements. The sustainability challenges in the investigated area are associated with productivity, economic viability and social acceptability. This research suggests some practices to achieve sustainable development in the study area for instance practicing farmers on modern ways of well management and soil conservation, increase level of health and school care, facilitation of loans for farmers and increasing markets number. outputs from this study can provide decision makers with valuable data that help them to ensure achieving of sustainable management within study area. [ABSTRACT FROM AUTHOR]

The Halayeb area is one of the most important Egyptian places, occupying a portion of the country's southeastern desert zone and serving as a strategic border with Sudan. The major goal of this study is to combine existing data on landforms, soil qualities, and climate data to define agro-ecological zones (AEZ) that are suitable for agricultural development. Thirty-two soil profiles were dug throughout the study region to represent physiographic units, and a digital soil map was generated based on an analysis of the digital elevation model and Landsat 8 satellite data, as well as climatic data from nine sites within the study area. Using the Model Builder in ArcGIS software, AEZ were created based on overlay maps of topography, soil chemical and physical properties, temperature, and precipitation maps. Zone (I) represents highly suitable areas (2.6 percent), zone (II) represents suitable areas (12.4 percent), zone (III) represents moderately suitable areas (26.5 percent), and zone (IV) represents marginally suitable areas (37.3 percent). highly suitable(S1), suitable (S2), and moderately suitable(S3) were the most appropriate classes for all of the crops studied. The findings will aid decision-makers in developing various development plans based on the research area's conditions. The study's methodology and findings can be used to evaluate which land is best suited for agricultural productivity growth. [ABSTRACT FROM AUTHOR]

Assessing soil quality marks the initial step in precision farming and agricultural management. Developing countries like Egypt face numerous hurdles in ensuring food security due to increasing populations and limited agricultural resources. A geographic information system (GIS) and multivariate analysis were utilized in the current work to evaluate and map a soil quality index (SQI). Moreover, the land suitability of the land for two plantations of the tree's oak ( Quercus robur ), and pine ( Pinus silvestris ), respectively was assessed using a parametric approach. A total of 82 soil profiles were selected to fulfill the objectives of the study. Based on the samples' PC scores, and agglomerative hierarchical clustering (AHC, the data was divided into two clusters: Cluster I and Cluster II, which collectively account for approximately 57% and 43% of the total data, respectively.. . The findings indicated that land suitability for planting Q. robur planted identified 2.14% of the research area as highly suitable (S1), 37.98% as moderately suitable (S2), and 59.89% as not suitable (N). Furthermore, the assessment of suitability for P. silvestris indicated that 50.88% of the investigated area was classified into: S1, 48.73% as S2, and 0.39% as N, which means it is not suitable for conservation activities. The research identified that soil depth beside excessive salinity and calcium carbonate as the primary soil constraints in the area in both clusters. The average soil depth, ECd and CaCO3 were 113.62 ± 12.41, 17.27 ± 10.23, 16.83 ± 6.57 in Cluster 1 and 45.43 ± 15.21, 22.42 ± 12.43, 21.55 ± 5.63 in Cluster II. The study demonstrates that integrating multivariate analysis with GIS enables a precise and streamlined assessment of the Soil Quality Index (SQI). Soil suitability modelling underscores the importance of implementing efficient management practices to attain agricultural sustainability in arid regions, particularly amidst intensive land utilization pressures., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)