Ibraheem Atya Hussien Yousif

Purpose: Sandy soils in arid regions have low fertility, poor structural stability, and limited water‐holding capacity. This study evaluated phosphogypsum (PG) as a soil amendment for improving sandy‐soil quality and wheat productivity under arid conditions. Methods: A field experiment was conducted during the 2020/2021 winter season in West El‐Minia, Egypt. PG was applied at 0, 10, 20, and 30 t ha ⁻¹ by surface broadcasting or soil incorporation. Soil physical, chemical, and biological properties, wheat yield, and nutrient uptake were evaluated. Results: Treatment performance depended on the management objective. Soil incorporation at 30 t ha ⁻¹ (T4M4) produced the strongest overall improvement in most soil and microbial indicators, whereas surface application at 20 t ha ⁻¹ (T3S3) produced the highest numerical grain yield and nutrient uptake. Under T4M4, bulk density decreased from 1.61 to 1.46 g cm ⁻³ , field capacity increased from 16.0% to 25.16%, available water rose from 11.0% to 19.65%, soil pH declined from 8.2 to 7.8, and cation exchange capacity increased from 4.22 to 9.08 cmolc kg ⁻¹ . Microbial biomass carbon and fluorescein diacetate hydrolysis activity increased by 327.7% and 107.6%, respectively. Grain‐yield increases ranged from 29.5% to 60.3% relative to the control. Conclusions: Surface application at 20 t ha ⁻¹ was the most effective yield‐oriented treatment, whereas incorporation at 30 t ha ⁻¹ provided broader soil and microbial improvement. PG recommendations should therefore be selected according to the intended management objective rather than a single generalized optimum.

Groundwater vulnerability refers to an aquifer’s susceptibility to contamination based on natural hydrogeological properties, including geology, soil, topography, and unsaturated zone characteristics. In low-recharge arid systems, limited recharge reduces dilution and flushing, allowing contaminants introduced through anthropogenic activities to persist over time. This study assesses groundwater vulnerability in El-Farafra, El-Kharga, and Tushka using a GIS-based DRASTIC approach, enhanced with a Land-Use DRASTIC model to incorporate human activities. Parameters, including the depth-to-water table, net recharge, aquifer media, soil media, topography, the vadose zone, and hydraulic conductivity, were spatially analyzed to generate vulnerability indices. Sentinel-2 imagery was used for land-use classification. In addition, expert elicitation from twenty hydrogeology specialists provided alternative parameter weightings, which were compared with the standard DRASTIC weights. Results show that incorporating land use and expert-based weights refines vulnerability patterns, particularly in agricultural, urban, and industrial zones. El-Farafra exhibits the highest vulnerability due to intensive land use and hydrogeological conditions, El-Kharga shows moderate vulnerability, and Tushka shows lower vulnerability, where recharge from Lake Nasser enhances dilution and reduces contaminant persistence. The study highlights the importance of integrating land-use information and expert knowledge to improve vulnerability assessment in data-scarce arid environments and supports improved groundwater management strategies.

In the summer of 2021, Algeria faced severe heat waves that led to extensive forest fires, particularly impacting the wilaya of Khenchela. Although forest fires occurred throughout the entire summer, the most intense and destructive events were recorded in July 2021, causing extensive ecological damage. This study focuses on mapping and assessing the damage caused by the summer 2021 forest fires in Khenchela, using Landsat satellite data and the Differential Normalized Burn Ratio. The analysis identified that the Ouled Yakoub and Beni Oudjana forests, located in the communes of Tamza and Chelia, were the most affected. The total burned area in Khenchela exceeded 9800 ha, with Ouled Yakoub losing over 6400 ha and Beni Oudjana more than 3000 ha, together accounting for approximately 97% of the total burned area. The 2021 fire was the most destructive event in the region from 2011 to 2021. Normalized Difference Vegetation Index (NDVI) mapping before and after the fire revealed an almost total absence of vegetation within the burned areas, reflecting the severe impact of the fire on vegetation cover. In contrast, non‐affected areas showed a slight increase in NDVI, likely due to seasonal vegetation recovery during the early autumn period. According to the detailed analysis of climatic parameters, 2021 was identified as the hottest and driest year in the past 11 years, with a significant rainfall deficit of −35.02%, and notable increases in average, maximum, and minimum temperatures by +1.25°C, +1.50°C, and +0.55°C respectively. Importantly, the study suggests that these extreme climatic conditions contributed to the severity of the fire and, in the context of climate change, such conditions may become more frequent. This highlights the need for improved fire monitoring and intervention strategies.

During the current study, Stevia rebaudiana leaves were dried using a photovoltaic thermal–indirect solar dryer (PVT-ISD), and the influence of tray position inside the drying chamber was assessed using six trays under two airflow rates (0.08 and 0.13 m ³ /s) with a uniform layer thickness of 3 cm. The fastest drying occurred on the lowest tray, closest to the hot air inlet, particularly at the higher airflow rate, achieving a final moisture content of 5.7%. Energy analysis showed that thermal efficiency of the solar collector reached up to 55.7% at the higher airflow. Exergy analysis supported this improvement, with maximum exergy efficiency of the solar collector increasing to 14.68%. Conversely, the drying room performed better at the lower airflow, reaching a maximum exergy efficiency of 37.89%. Sustainability indicators revealed slight improvements in the solar collector but declines in the drying room at higher airflow. Environmental indicators further demonstrated enhanced performance of the solar collector at higher airflow, with maximum reductions observed in the environmental destruction coefficient (19.6%), environmental impact factor (20.6%), and environmental effect factor (20.4%).

Introduction: Desertification risk is one of the main environmental, social and economic issues affecting sustainable development and food security in arid and semi-arid regions. Materials and methods: This study aims to use geospatial information to evaluate and map the environmental sensitivity areas for desertification (ESAs) in Naser Al-thawra Village area, New Valley, Egypt, using the modified Mediterranean Desertification and Land Use Model (MEDALUS). We combined Current and Potential Land Productivity Indicators (CLPI/PLPI) to link desertification risk with food production capacity. Results and discussion: The obtained data reveals that 36.73% of the study area is characterized by high and very sensitive areas to desertification (Critical type of ESA), moderate sensitive areas about 23.39% (Fragile type), while the no sensitive/very low sensitive areas (Non-Affected type) occupies approximately 25.39% of the study area. For land productivity assessment results showed that for Current Land Productivity Index (CLPI) most of the study areas 43.73%; are poor classes (class IV) in terms of agricultural use, the 22.65% are extremely poor class or nil (class V) and 19.14% are average area (class III). Also, for Potential Land Productivity Index (PLPI) 10.44% of the study area are excellent area (class I), the 35.47% are good (class II), the 16.96% are average (III) and 22.65% are poor area (class IV). Furthermore, our comprehensive analysis identifies a substantial opportunity: about 45.9% of land defined as critically susceptible to desertification simultaneously exhibits good-to-excellent potential productivity (PLPI Classes I-II). This delineates high-priority areas where focused interventions may simultaneously reduce desertification and improve food production. The research directly supports the United Nations Sustainable Development Goals (SDGs), particularly Goals 2 (Zero Hunger), 13 (Climate Action), and 15 (Life on Land), by advancing approaches that mitigate land degradation and enhance agricultural resilience in arid regions. Its outcomes strengthen the foundations of food security through sustainable land management and climate-adaptive planning.

Background: Assessing the quality of the soil is a crucial first step in agricultural management. A major obstacle to raising agricultural output, economic growth, and environmental health has been the decline in soil quality. One of the most often used metrics for evaluating soil quality is the soil quality index (SQI), which is frequently calculated using principal component analysis (PCA). Methodology: In this study, a soil quality index in the southwest of the Ismailia Governorate, Egypt, was evaluated and mapped using a geographical information system (GIS) and multivariate analysis. (PCA). Fifty- one soil samples were gathered for this purpose, and they were examined using established procedures. The dataset was broken down into new variables using principal component analysis (PCA) to avoid multi-collinearity. Relative weights (Wi) and soil indicators (Si) were then established and used to calculate SQI. The SQI comprises three quality zones. Results and discussion: The first zone has a very good quality index, accounting for about 65.66 (ha) of the entire area. Soils in this zone were defined by low salinity of the groundwater and adequate values of each soil attribute. The second zone, which makes up about 414.76 ha (67.5%) of the total area, is characterized by its good-quality soil. About 133.91 ha (21.8%) of the total land area is in the third zone, which is fair (bad quality). Low concentrations of soil organic matter (SOM), salinity, accessible nitrogen (N), phosphorus (P), potassium (K), and cation exchange capacity (CEC) had the greatest effects on the SQI of the studied location. Combining PCA and GIS enables a precise and efficient evaluation of the SQI. Conclusion: Decision-makers can identify regions with very good, good, and poor soil quality by examining the generated spatial distribution maps. Additionally, they can learn how each characteristic influences plant growth. In addition, The methodology outlined in this work can be readily replicated in similar situations in arid regions, enabling local authorities and decision-makers to make use of the quantitative results achieved to guarantee long-term development.

Desertification is one of the greatest environmental issues affecting sustainable development and food security in arid and semi-arid regions. Due to the dry conditions and dependence on groundwater as the main irrigation source, the West El-Minya region of Egypt is particularly susceptible to the increasing hazards of desertification. This research study aims to evaluate and map the environmentally sensitive areas (ESAs) to desertification in this study region using the modified Mediterranean Desertification and Land Use Model (MEDALUS) approach supported by sophisticated geospatial techniques. The model was utilized as a case study in the West El-Minya region of Egypt, covering an area of 447.12 km ² (44712.54 ha). The five thematic quality indices of soil (SQI), geomorphology (GQI), climate (CQI), vegetation (VQI), and management (MQI) were characterized through the analysis of satellite images, climatic data, field observations, and soil samples taken from 150 soil profiles. All these indices were integrated in a geographical information system (GIS) framework for ESAs in relation to desertification. According to the model, the findings indicated that the whole area (100%) is within the Critical type of ESA, with no area being assessed as the “Non-Affected” or “Fragile” or “Potential” type. A total of 84.79% (37911.99 ha) of the study area is classified as Critical sub-type 2, while 12.74% (5698.51 ha) and 2.46% (1102.04 ha) are classified as sub-type 3 and sub-type 1, respectively. This is mainly due to the arid climatic conditions and very low vegetation cover, with a strongly negative impact on the study area. The results of the study confirm the urgent need to develop integrated policies for managing natural resources. This study illustrates an innovative application of the MEDALUS model with the addition of geomorphological parameters and applies it in an under-researched area of Egypt. It also presents an explicit spatial assessment that can be utilized to support policy decision-making for land degradation mitigation and sustainable land management.

Rapid population expansion has made food security a global concern for humanity, necessitating a sustainable assessment of natural resources. Well evaluated and managed soil is one of the most significant resources that can assist close the gap between supply and demand for food to attain food security. A precise assessment of land productivity (LP) is essential for sustainable land use management. The primary aim of this research is to predict the land productivity index (LPI) for certain locations on Egypt’s northwest coast under current conditions using soil quality indicators (SQIs). Additionally, the study proposed relevant management actions to boost land productivity in the future. To achieve this aim, a spatial model was created to assess LPI for some areas in the north western coast of Egypt. LP equation was used to estimate current land productivity index (CLPI) and potential land productivity index (PLPI). Results found that the study area mainly located under two classes of CLPI, e.g., average (class ⅡⅠ) represents an area of 13322.63 ha (67.55%) and good grade (class Ⅱ) covers an area of 4339.78 ha (22%). PLPI grades were good (II) and average (III) with an area of 16192.54 ha (82.10%) and 1903.23 ha (9.65%), respectively. The finding showed that more than 60% of the studied area was converted from poor and average class to good class after applying the proposed solutions of soil improvement. The methodology presented here can easily be replicated under similar circumstances in arid zones, allowing local governments and decision-makers to utilize the quantitative results obtained to ensure sustainable development.

Assessing and mapping the geographical variation of soil properties is essential for precision agriculture to maintain the sustainability of the soil and plants. This study was conducted in El-Ismaillia Governorate in Egypt (arid zones), to establish site-specific management zones utilizing certain soil parameters in the study area. The goal of the study is to map out the variability of some soil properties. One hundred georeferenced soil profiles were gathered from the study area using a standard grid pattern of 400 × 400 m. Soil parameters such as pH, soil salinity (EC), soil organic carbon (SOC), calcium carbonate (CaCO₃), gravel, and soil-available micronutrients (Cu, Zn, Mn, and Fe) were determined. After the data were normalized, the soil characteristics were described and their geographical variability distribution was shown using classical and geostatistical statistics. The geographic variation of soil properties was analyzed using semivariogram models, and the associated maps were generated using the ordinary co-Kriging technique. The findings showed notable differences in soil properties across the study area. Statistical analysis of soil chemical properties showed that soil EC and pH have the highest and lowest coefficient of variation (CV), with a CV of 110.05 and 4.80%, respectively. At the same time Cu and Fe had the highest and lowest CV among the soil micronutrients, with a CV of 171.43 and 71.43%, respectively. Regarding the physical properties, clay and sand were the highest and lowest CV, with a CV of 177.01 and 9.97%, respectively. Moreover, the finest models for the examined soil attributes were determined to be exponential, spherical, K-Bessel, and Gaussian semivariogram models. The selected semivariogram models are the most suitable for mapping and estimating the spatial distribution surfaces of the investigated soil parameters, as indicated by the cross-validation findings. The results demonstrated that while Fe, Cu, Zn, gravel, silt, and sand suggested a weak spatial dependence, the soil variables under investigation had a moderate spatial dependence. The findings showed that there are three site- specific management zones in the investigated area. SSMZs were classified into three zones, namely high management zone (I) with an area 123.32 ha (7.09%), moderate management zone (II) with an area 1365.61ha (78.49%), and low management zone (III) with an area 250.8162 ha (14.42%). The majority of the researched area is included in the second site zone, which represents regions with low productivity. Decision-makers can identify locations with the finest, moderate, and poorest soil quality by using the spatial distribution maps that are produced, which can also help in understanding how each feature influences plant development. The results showed that geostatistical analysis is a reliable method for evaluating and forecasting the spatial correlations between soil properties.