Search Results (2,099)

The desert ecosystem harbors a resilient microbial community that sustains plant life under extreme stress. Understanding the endophytic microbiota of desert flora provides key insights into how these microorganisms enable plant survival and maintain ecological balance in arid landscapes. To date, the endophytic bacterial communities of dominant desert plants in the Arabian Peninsula have not been comprehensively characterized. Here, we investigated the endophytic microbiota of five co-adapted desert species, namely, Schweinfurthia papilionacea, Sesuvium verrucosum, Ochtocloa compressa, Helianthemum nummularium, and Convolvulus arvensis. These plants coexist in hyper-arid habitats and exhibit exceptional tolerance to drought, salinity, and nutrient scarcity. We hypothesized that, despite their phylogenetic divergence, these plants host functionally convergent microbial communities shaped by desert selection pressures. Using 16S rRNA gene amplicon sequencing, we obtained 3.4 million high-quality reads from 25 samples. Clustering at 97% similarity revealed 35 phyla and 17 dominant genera, highlighting notable microbial richness and ecological complexity. Alpha-diversity indices showed comparable species richness across hosts, while beta-diversity indicated community differentiation driven by environmental filtering. The dominant phyla included Pseudomonadota, Actinomycetota, Cyanobacteriota, and Bacillota, reflecting microbial adaptation to extreme desert conditions. Functional pathway prediction revealed enrichment of genes associated with DNA repair and protein turnover, suggesting metabolic flexibility and enhanced survival under stress. Overall, this study provides a comparative metagenomic insight into the endophytic bacterial communities of five desert plant species, uncovering a consistent pattern of functional convergence across diverse hosts. The findings suggest the presence of shared functional traits among the endophytic microbiota examined here, offering preliminary evidence for microbial contributions to plant resilience in arid environments. Full article

Chickpea (Cicer arietinum L.) is a globally important legume crop whose grain yield is strongly influenced by environmental and agronomic variability. This study aimed to predict chickpea grain yield using artificial neural networks (ANNs) and to identify key traits associated with yield formation across different genotypes under semi-arid conditions. The dataset consisted of 96 chickpea genotypes evaluated over two growing seasons (2022–2023) in Sivas, Türkiye. The results demonstrated that reproductive traits, particularly seed weight per plant, number of pods per plant, and number of seeds per plant, were the most influential factors determining grain yield. Environmental variability also contributed significantly to yield prediction, highlighting the importance of genotype–environment interactions. The developed ANN model showed high predictive accuracy, indicating its robustness in capturing complex relationships among yield-related traits. Beyond prediction, the model provides biologically meaningful insights into trait prioritization, supporting its application in chickpea breeding programs. Overall, the findings suggest that ANN-based approaches can serve as effective decision-support tools in precision agriculture by enabling accurate yield estimation, facilitating the selection of high-performing genotypes, and identifying key breeding traits for sustainable crop improvement. Full article

Spatio-temporal analysis of land cover (LC) dynamics is essential for understanding landscape transformation in semi-arid woodland ecosystems. This study assessed historical and projected land cover changes in the Elnour Natural Forest Reserve (ENFR), Sudan, from 1995 to 2060. Historical maps for 1995, 2008, and 2021 were generated using a Random Forest classifier, while future scenarios for 2034, 2047, and 2060 were simulated using a Cellular Automata–Artificial Neural Network (CA–ANN) model. The results show that semi-bare land expanded from 23.1% in 1995 to 40.0% in 2021, while dense woodland declined from 26.7% to 15.7%, indicating substantial structural transformation of the landscape. Open woodland exhibited partial recovery, increasing to 39.9% in 2021. Future projections indicate a moderate increase in dense woodland to 23.8% by 2060; however, semi-bare land remains the dominant class, reflecting persistent landscape instability. These findings demonstrate the coexistence of degradation and localized regeneration processes in ENFR and highlight the importance of long-term monitoring of land cover dynamics in dryland environments. The study further shows that integrating machine learning classification with spatially explicit CA–ANN modeling provides an effective framework for analyzing historical trends and exploring potential future trajectories of land cover change in data-limited semi-arid regions. Full article

This study aims to identify agricultural land suitability in Mardin province, located in the semi-arid Southeastern Anatolia Region of Türkiye. Within this framework, eight ecological criteria were selected to assess agricultural land suitability. Criterion weights were derived from expert judgments using the Analytical Hierarchy Process (AHP), a Multi-Criteria Decision-Making (MCDM) method. The criteria were evaluated within the framework of the five classes used in agricultural land-use suitability, in accordance with the guidelines of the Food and Agriculture Organization of the United Nations (FAO). Based on this classification, maps of the determined criteria were prepared using Geographic Information Systems (GISs), and an agricultural land-use suitability map was produced using a weighted overlay approach. The results indicate that 31.3% of the total land area in Mardin province falls within the highly and moderately suitable classes. For validation, the suitability map was overlaid with the Coordination of Information on the Environment (CORINE) Land Cover (CLC) 2018 data, revealing that 98.8% of highly suitable (S1) areas and 94.6% of moderately suitable (S2) areas correspond to existing agricultural lands. Furthermore, Receiver Operating Characteristic (ROC) analysis yielded an Area Under the Curve (AUC) value of 0.815, indicating an acceptable-to-good discrimination ability and confirming the robustness of the model. Full article

This research evaluated the efficacy of using two types of biochar (non-modified and acidified) from date palm residues (fronds, leaves, pits) as soil amendments for enhancing soil fertility and maize growth. These biochars were produced through slow pyrolysis under oxygen-limited conditions at 500 °C. Our innovative approach was to minimize gas emissions by converting smoke into liquid fertilizer (LS), which was expected to improve seed germination and early plant growth stages. To assess this aim, a completely randomized experiment was conducted under lab conditions, in which 10 maize seeds were placed on double filter papers in Petri dishes and then exposed to seven concentrations of LS (0.0, 0.01, 0.10, 1.0, 10 and 100%, using distilled water for dilution v/v). The LS contains nutrients and bioactive compounds that may enhance seed germination and early plant growth at low concentrations, whereas higher concentrations may cause phytotoxic effects. Results showed that liquefied smoke at 0.1% increased the absolute percentage of maize germination from 75% (control) to 100% and achieved the highest root length of 9.80 cm. Acidified biochars at 5% reduced soil pH from 8.87 to 8.12 and enhanced potassium availability to 87.93 mg kg⁻¹. Conversely, the non-modified biochars contributed to further increases in soil organic matter (up to 1.02%), nitrogen, and phosphorus. In addition, the application of acidified leaf biochar (5%) enhanced maize shoot growth by 133%, chlorophyll content by 39%, and potassium uptake by 110%. This research establishes a scalable approach for converting agricultural waste into climate-resilient resources, effectively addressing soil degradation in arid environments, boosting crop resilience, and furthering the objectives of a circular bioeconomy. Full article

Sustainable groundwater management in hyper-arid regions requires accurate water quality assessments, yet remote desert environments present major challenges due to data scarcity, high sampling costs, and limited laboratory infrastructure. This study proposes a framework integrating the Water Quality Index (WQI) with Inverse Distance Weighting (IDW)-based spatial data augmentation and machine learning classification for groundwater quality assessment in the Tabelbala region, southwestern Algeria. Three classifiers were evaluated, Random Forest (RF), Support Vector Machines (SVMs), and Artificial Neural Networks (ANNs), and trained on an augmented dataset generated from 178 original groundwater samples using IDW interpolation with a sensitivity-optimized 150 m radius, producing 2779 augmented training points. RF achieved the highest predictive accuracy (85.9%), followed by ANNs (84.7%) and SVMs (83.1%), with all models demonstrating excellent discriminative performances (area under the receiver operating characteristic curve > 0.96). Permutation Feature Importance analysis identified total dissolved solids (TDS), sulfates (SO₄²⁻), total hardness (TH), and chlorides (Cl⁻) as the most influential parameters, consistent with World Health Organization (WHO) guidelines. Spatial distribution maps revealed that the majority of groundwater sources exhibited poor to very poor quality, highlighting the urgent need for local water management interventions. The proposed framework offers a replicable decision-support tool for water resource managers in data-scarce arid environments, supporting SDG 6 (Clean Water and Sanitation) and SDG 13 (Climate Action). Full article

Salt, drought and freezing stress were major abiotic factors limiting plant growth, development and yield. Halostachys caspica (Amaranthaceae), a halophyte native to saline-arid desert regions, tolerated multiple abiotic stresses, but its molecular mechanisms of stress tolerance remain unclear. By integrating the small RNA library and transcriptome data of H. caspica under high salinity, HcmiR172e was identified as a differentially expressed miRNA and selected for the study of multiple abiotic stress responses. Using its mature sequence (20 nt) to align with upregulated genes from the transcriptome, HcTOE3 (AP2 subfamily transcription factor belonging to the AP2/ERF family) was preliminarily predicted as its target gene through bioinformatic analysis. Our previous work demonstrated that HcTOE3 was strongly upregulated by multiple abiotic stresses, including salinity, drought, heat and low temperature. Furthermore, overexpression of HcTOE3 conferred freezing tolerance to Arabidopsis throughout the entire growth period. In this study, miRNA expression analyses showed that HcmiR172e was significantly downregulated in the assimilating branches of H. caspica under low temperature, heat, salt, drought, oxidative stress and abscisic acid (ABA) application. Tobacco transient expression assays and 5′RLM-RACE confirmed that HcmiR172e directly cleaved HcTOE3 transcripts in the region close to the 5′end of the ORF. HcmiR172e-overexpressing Arabidopsis displayed increased sensitivity to salt, drought, freezing stresses and ABA treatment, along with enhanced growth inhibition, elevated reactive oxygen species (ROS) accumulation, decreased osmolyte content and downregulation of stress-responsive genes. In contrast, HcTOE3-overexpressing Arabidopsis exhibited the opposite phenotypes, physiological responses and corresponding gene expression patterns under multiple stress treatments. These findings collectively elucidated the antagonistic regulatory roles of HcmiR172e and HcTOE3 in plant abiotic stress responses, providing novel molecular targets for engineering stress-tolerant crops for saline, arid, freezing environments. Full article

The digital and green transitions in the AEC sector require rapid, data-driven workflows to redefine sustainability through real-time performance evaluation. However, the high computational cost of traditional energy simulations often lacks evidence-based feedback during early-stage design. This study introduces a surrogate machine learning framework (S-TCML) designed to bypass traditional energy simulation by providing an instantaneous assessment of thermal comfort. Using a parametric Grasshopper–Honeybee environment, a dataset of 3072 configurations was generated for an office room in Cairo, Egypt. Six machine learning algorithms were benchmarked, with Gradient Boosting and Random Forest demonstrating superior performance in capturing non-linear thermal physics. Validation against the EnergyPlus engine confirmed that S-TCML models deliver predictions in milliseconds—a 99.9% reduction in computational time. The Gradient Boosting model achieved exceptional accuracy with an R² of 0.999 and RMSE of 0.013 for PMV and an R² of 0.995 and RMSE of 0.46% for PPD prediction. Feature importance analysis proved that a tree-based ML model can capture the underlying physical relationship between variables. To bridge the feedback gap, a web-based graphical user interface (GUI) was developed to facilitate proactive design exploration. This framework supports sustainable decision-making and design efficiency, offering scalable, user-friendly tools that protect occupant health and ensure thermal resilience in hot–arid environments. Full article

The Numidian cypress (Cupressus sempervirens var. numidica, C. numidica hereafter) is a rare, almost unknown, endemic taxon of Tunisia whose conservation has long been hampered by human activities, taxonomic uncertainty and limited ecological knowledge, with only 64.33 ha of its populations remaining. Although recent genetic studies have confirmed its native status and long-term isolation, detailed information on its distribution, population structure and threats remain lacking. This study provides the first comprehensive assessment of C. numidica across its remaining range. Field surveys revealed that the species persists in only three small, fragmented forests, Bou Abdallah, Sidi Amer, and Dir Satour, covering a total of 64.33 ha. Soil analysis revealed some differences among sites, with Bou Abdallah showing higher clay content and Dir Satou exhibiting the highest levels of nitrogen, organic carbon, Olsen P, and available Mn and Mo. Climatic analyses indicate a semi-arid Mediterranean environment with pronounced summer droughts and a clear warming trend. Trees showed widespread damages, due to intensive grazing, tree cutting, crown dieback (drought), and pest and pathogen attacks. Natural regeneration was limited, and the condition of affected trees ranged from moderate to severe, with Bou Abdallah showing the highest levels of degradation. Notably, the severe fungal pathogen Seiridium cardinale, causal agent of cypress canker, was detected on C. numidica for the first time, highlighting an urgent conservation concern. Our results point to a staged conservation approach over time. In the immediate term (within 1 year), urgent monitoring and management of S. cardinale is needed. In the short term, efforts should focus on protecting carefully selected areas, about 5–10 regeneration microsites per forest, from grazing to support natural regeneration, reduce ongoing soil degradation, and establish clonal and seed-production plantations along with long-term seed storage. In the long term, the survival of C. numidica will only be possible with the active involvement of local communities, through awareness campaigns, adapting traditional practices such as gdel, and developing small-scale ecotourism that provides sustainable livelihoods while reinforcing support for conservation. Full article

The Turpan–Hami Basin, a critical energy hub in northwestern China, is plagued by frequent ground collapses induced by extensive mining over karst geology, threatening ecology and safety. Current hazard assessment methods, mainly single linear or traditional machine learning models, fail to capture the complex nonlinear interactions inherent to this coupled geo-mining environment. This study addresses this gap by establishing a multi-dimensional “Geology-Mining-Hydrology-Environment” index system comprising 14 critical factors—including lithology, goaf distribution, mining intensity, and their interaction terms. A coupled gradient boosting decision tree and logistic regression (GBDT-LR) model, optimized for the multi-factor coupling characteristics of ground collapse in arid mining basins, was applied for the hazard assessment. The results reveal a distinct spatial pattern of “core agglomeration with multi-level gradient differentiation.” Extremely high-hazard areas, covering 9.21% of the area, are concentrated in the core mining areas northwest of Turpan and southwest of Hami, while high-hazard areas (4.63%) form surrounding belts. The GBDT-LR model (AUC = 0.871) demonstrated significantly superior performance over a single logistic regression model (AUC = 0.813), proving its enhanced capability to identify high-hazard areas by modeling complex factor interactions. This work provides an essential scientific foundation for implementing zonal hazard management and prioritizing disaster prevention projects in key areas of the basin. Full article

Aiming at the problems of a fragile ecological environment, water shortage and system uncertainty in inland arid irrigation districts in Xinjiang, this study takes sustainable development as the guide, selects the Tailan River Irrigation District in Xinjiang as an example, and constructs a multi-objective optimal allocation model of agricultural water and soil resources in irrigation districts driven by water–carbon–economy synergy. The model aims to minimise irrigation water shortage, maximise crop carbon absorption and maximise economic benefits. By comparing six multi-objective algorithms such as APSEA, CMEGL, DCNSGA-III, DRLOS-EMCMO, MOEA/D-CMT and θ-DEA-CPBI, the optimal is selected based on the hypervolume (HV) index. The surface water, groundwater and crop-planting structure of five decision-making units in the irrigation district from 2021 to 2024 were optimised. Further, combined with the entropy weight–TOPSIS coupling-coordination comprehensive-evaluation model, the scheme evaluation system is constructed to screen the optimal configuration scheme of each year and unit. The results show that the MOEA/D-CMT algorithm has the highest HV value in each unit model over the years, which is the best solution algorithm for the model in this paper. The comprehensive evaluation value and coupling coordination degree of the optimal scheme of each unit fluctuate between years, and the difference between units is significant. Compared with the original planting and water source allocation scheme of the irrigation district from 2021 to 2024, the overall planting area of the optimised irrigation district is moderately reduced, forming an optimised pattern of ‘cotton pressure, grain expansion, economic increase and strong forest’; after optimization, the overall water shortage in the irrigation district is reduced by 1.4~11 million m³; the total amount of crop carbon absorption increased by 90.3~128.8 million kg; the net economic benefits increased by CNY 21.5~68.2 million. The research can provide decision support for the optimisation of the water and soil resource system in arid irrigation districts and has a scientific reference value for promoting the sustainable development and modernisation of agriculture in the inland irrigation districts of Northwest China. Full article

Wastewater treatment plants (WWTPs) are increasingly considered critical infrastructure for environmental protection and combating climate change in regions suffering from severe water scarcity. The aim of this work is to provide a comprehensive and integrated evaluation of the performance of WWTPs in arid and hyperarid contexts, based on two representative experiences in the Algerian Sahara. The evaluation is based on an analysis of treatment performance (COD, BOD₅, TSS), operational stability, and the agricultural suitability of the wastewater (electrical conductivity, SAR, RSC), in addition to the indirect effects on groundwater protection. The results show high and stable organic matter removal rates (>85–90%), demonstrating the effectiveness of biological processes under harsh climatic conditions. Despite these benefits, residual salinity and sodium carbonate remain the two main factors limiting the extent of long-term agricultural reuse, despite effective treatment. The international comparative analysis highlights the systemic nature of this dissociation in hyperarid environments and emphasizes the need to consider wastewater treatment plants as truly integrated environmental barriers. Full article

Floods are among the most frequent and destructive natural hazards in Kazakhstan, particularly in the Abai Region, Kazakhstan, where topographic, hydrological, and climatic factors strongly influence flood occurrence. This study presents a comprehensive spatial assessment of flood susceptibility in the Abai Region using a multi-criteria Geographic Information System (GIS) approach. The analysis integrates twelve flood-conditioning factors representing hydrological, topographic, environmental, and anthropogenic variables. The relative importance of these factors was determined using the Analytical Hierarchy Process (AHP). The results indicate that distance to rivers (20%) and precipitation (16%) are the most influential drivers of flood susceptibility, followed by Height Above Nearest Drainage (HAND) (11%) and drainage density (9%). The resulting flood susceptibility map classifies the study area into five susceptibility levels. Approximately 56.6% of the study area falls within the moderate susceptibility class, while 25.0% is categorized as high susceptibility, mainly concentrated in low-lying floodplains and foothill regions. Low-susceptibility areas account for 18.1% of the region, whereas the very high and very low susceptibility classes together represent less than 1% of the territory. Model performance was evaluated using Receiver Operating Characteristic (ROC) analysis, yielding an Area Under the Curve (ROC–AUC) value of 0.893, indicating strong agreement between predicted susceptibility patterns and observed flood occurrences. Additional validation metrics derived from the confusion matrix show an overall accuracy of 83.3%, precision of 0.75, recall of 1.0, and a Kappa coefficient of 0.67, confirming reliable predictive performance. Sensitivity analysis with ±10% variation in factor weights further demonstrated the spatial stability of the susceptibility results. The resulting susceptibility map provides an important spatial basis for infrastructure planning, flood mitigation, and disaster preparedness in the Abai Region and offers a transferable framework for flood-susceptibility assessment in other semi-arid regions of Central Asia. Full article

Photovoltaic (PV) performance in desert environments is significantly hindered by soiling and partial shading. To bridge the gap in empirical data for extreme Saharan conditions, this study presents a novel techno-economic assessment of uniform horizontal shading (UHS) specifically conducted in Mauritania. Controlled outdoor experiments were performed using a 250 W crystalline silicon PV module and a PVPM 2540C I–V curve tracer, applying progressive shading levels from 2.5% to 20%. The novelty of this work lies in the integration of high-resolution experimental I–V/P–V characterization with a localized techno-economic model for five pre-commercial PV plants. It was observed that PV modules are exceptionally sensitive to shading; specifically, a mere 10% shaded area leads to a catastrophic 90% drop in power and current, while the voltage remains remarkably stable. Thermographic analysis further validates the thermal gradients and bypass diode functionality. By quantifying the financial impacts, this research highlights that cumulative economic losses across the five real-world sites reached 87.95%, exceeding 55,000 MRU. These findings provide a strategic framework for optimizing PV systems in arid terrains and offer a robust tool for enhancing the design and operation of large-scale solar applications in desert environments. Full article

Soil CO₂ emissions are critical for predicting terrestrial ecosystem feedbacks to climate change, yet significant knowledge gaps persist regarding carbon flux dynamics within the deep vadose zone and during freeze–thaw processes. In this study, the Mu Us Sandy Land, a representative seasonally frozen and semi-arid region in Northwestern China, was selected as the research site. Based on in situ observation data and the XGBoost algorithm, the spatiotemporal variations of soil carbon flux and its environmental drivers were investigated. Results revealed distinct depth-dependent patterns, where carbon release reached its maximum flux in the 100–200 cm layer and carbon sequestration dominated the soil layers below 200 cm. Soil temperature and moisture were the primary controlling factors, but their impacts exhibited significant depth and seasonal heterogeneity. Notably, in the 20–50 cm soil layer, soil water content provided the highest explanatory power, reaching 55.3% and 47.8% in winter and summer, respectively. Furthermore, carbon fluxes exhibited distinct response thresholds to environmental factors, and their spatiotemporal variations were fundamentally regulated by an atmosphere-driven coupled water–vapor–heat–carbon process. These findings elucidate the complex relationship between soil carbon fluxes and the environment at different depths, providing theoretical support for deepening the understanding of regional carbon cycling. Full article