Search Results (2,050)

Arid and semi-arid regions are highly vulnerable to drought and depend heavily on rainfed agriculture. To minimize the impact of drought, a transition from crisis management to risk management is necessary, which requires a comprehensive risk assessment that accounts for not only drought hazard but also drought vulnerability and population exposure. However, integrated studies that account for socio-economic, agricultural, demographic, and climate factors are currently lacking in Iran. The objective of this study is to comprehensively assess the spatio-temporal changes in drought risk from 2000 to 2019 across Iran. We used the standardized precipitation evapotranspiration index (SPEI) and multiple socio-economic and demographic data to compute drought risk. In particular, we used the SPEI to map drought hazard, an analytical hierarchical process method to assess drought vulnerability, and population density data to compute population exposure. Drought risk increased in 57% of the area of Iran, mainly in the northwest, west, and central regions, at a rate of up to 10% per year. In 21% of the area of Iran, drought risk declined by up to 10% per year, predominantly in the northern and southern regions of the Alborz Mountains, encompassing the provinces of Tehran, Gilan, Mazandaran, and Khorasan Razavi. Our results show that the spatial patterns of drought risk vary across Iran and are modulated by the interaction between climatic and socio-economic factors. The results of this study provide useful information for drought risk management and intervention in Iran. Full article

White lupin (Lupinus albus) is a very important legume crop, having seeds with high protein content but also quantities of antinutritional alkaloids. Regarding cultivation, it is sensitive to neutral or alkaline soil conditions, although it is well adapted to drought conditions. In this study, the adaptability of 17 L. albus (14 commercial varieties and 3 advanced lines) genotypes to neutral-pH soils was investigated in relation to morphological, agronomical, and yield attributes. An extended characterization of seed composition for total alkaloids, trypsin inhibitors, phenolics, tannins, total nitrogen, NDF, ADF, and lignin was also performed. Furthermore, a prebreeding program consisting of 140 targeted crosses was initiated to develop new F1 combinations for genotypes with low alkaloid profiles; at the same time, controlled self-fertilization of elite lines was carried out. The results indicated that the morphological response of L. albus to neutral pH was positive and significantly genotypically dependent. Among the varieties tested, ‘Magnus’ and ‘Figaro’ showed low alkaloid and lignin contents. On the other hand, the advanced lines (LKAP, LKML, LKAU) had high antinutritional components, even though they were high-yielding. This research proposes a model of combined evaluation and selection processes for identification of particular genotypes that can perform well in neutral soils and provides the basis for breeding and producing low-alkaloid genotypes for multi-locational exploitation. Full article

Dryland coastal–mountain systems stand at the frontline of climate change, where steep topographic gradients amplify the balance between resilience and collapse. Mount Elba—Egypt’s hyper-arid coastal–mountain reserve—embodies this fragile equilibrium, preserving a seventy-year climatic record across a landscape poised between sea and desert. Here, we present the first multi-decadal, spatio-temporal assessment (1950–2021) integrating the Standardized Precipitation–Evapotranspiration Index (SPEI-6) with satellite-derived vegetation responses (NDVI) along a ten-grid coastal–highland transect. Results reveal a pervasive drying trajectory of −0.42 SPEI units per decade, with vegetation–climate coherence (r ≈ 0.3, p < 0.05) intensifying inland, where orographic uplift magnifies hydroclimatic stress. The southern highlands emerge as an “internal drought belt,” while maritime humidity grants the coast partial refuge. These trends are not mere numerical abstractions; they trace the slow desiccation of ecosystems that once anchored biodiversity and pastoral livelihoods. A post-1990 regime shift marks the breakdown of wet-season recovery and the rise in persistent droughts, modulated by ENSO teleconnections—the first quantitative attribution of Pacific climate signals to Egypt’s coastal mountains. By coupling climatic diagnostics with ecological response, this study reframes drought as a living ecological process rather than a statistical anomaly, positioning Mount Elba as a sentinel landscape for resilience and adaptation in northeast Africa’s rapidly warming drylands. Full article

Sorghum (Sorghum bicolor L.) is a vital crop for both grain production and forage, playing a critical role in ensuring global food security and sustainable livestock production. Drought stress represents one of the most severe abiotic constraints in sorghum cultivation, adversely affecting plant growth and development, and ultimately leading to significant reductions in yield and quality. Melatonin has emerged as a multifaceted plant growth regulator that enhances plant growth and confers tolerance to various abiotic stresses. It actively participates in regulating key physiological processes, including seed germination, seedling establishment, cellular development, and metabolic homeostasis. This review synthesizes current knowledge on the impacts of drought stress on sorghum growth and physiological metabolism, with a specific focus on the protective role of melatonin under water-deficit conditions. The underlying physiological and molecular mechanisms are comprehensively discussed, encompassing ion homeostasis, nutrient metabolism, reactive oxygen species (ROS) scavenging, photosynthetic efficiency, energy metabolism, phytohormone crosstalk, signal transduction, and associated gene expression. Finally, we outline future research directions to advance our understanding of melatonin-mediated drought tolerance in sorghum, providing insights for breeding drought-resilient varieties and developing high-yielding cultivation strategies. Full article

The APETALA2/Ethylene Response Factor (AP2/ERF) family of transcription factors (TF) is characterized by their participation in various biological processes related to growth, development, and response to stress. ERFs are ideal candidates for crop improvement because they regulate defense genes like JERF1, JERF3, LeERF2, NtERF5, and Tsil which confer tolerance to drought, salinity, osmotic stress, and pathogen attack, respectively. The ERF subfamily includes the TF Pti4, whose activity is regulated by different signaling pathways, thus providing tolerance response to multiple factors such as drought, salinity, cold, and pathogen attack in tomato. In this work we evaluated the effect of overexpression of TF SlPti4 from Solanum lycopersicum in transgenic tobacco plants when subjected to saline, osmotic, and drought stress. Our results from this study demonstrated that transgenic lines overexpressing Pti4 tolerate abiotic stress during germination and in plants. The transgenic lines showed improvements in photoinhibition, electron transport rate, chlorophyll content, and biomass, as well as a reduction in malondialdehyde content. Full article

Agriculture faces increasing challenges in ensuring food security under a changing climate, where abiotic stresses such as salinity and drought represent major constraints to crop productivity. These stresses induce complex physiological and biochemical alterations in plants, including osmotic imbalance, oxidative damage, and disruption of metabolic pathways, ultimately impairing growth and yield. In this context, the application of biostimulants has emerged as a sustainable strategy to enhance plant resilience. While synthetic products are widely available, growing attention is being directed toward natural bio-based products, particularly those derived from renewable biomasses and organic wastes, in line with circular economy principles. This review critically examines the current literature on bio-based products with biostimulant properties, with particular emphasis on vermicompost-derived extracts, humic-like substances, and macro- and microalgae extracts, focusing on their role in mitigating salt and drought stress in plants. The reviewed studies consistently demonstrate that these bio-products enhance plant tolerance to abiotic stress by modulating key physiological and biochemical processes, including hormonal regulation, activation of antioxidant defence systems, accumulation of osmoprotectants, and regulation of secondary metabolism. Moreover, evidence indicates that these bio-based inputs can improve nutrient use efficiency, photosynthetic performance, and overall plant growth under stress conditions. Overall, this review highlights the potential of non-microbial bio-based biostimulants as effective and sustainable tools for climate-resilient agriculture, while also underlining the need for further research to standardize formulations, clarify mechanisms of action, and validate their performance under field conditions. Full article

The pressure of climate change on water resources has made the development of reliable hydrological models increasingly important, especially for data-scarce regions. However, due to the limited availability of ground-based observations, it considerably affects the accuracy of models developed using these inputs. This also limits the ability to investigate future hydrological behavior. Satellite-based data sources have emerged as an alternative to address this challenge and have received significant attention. However, the transferability of these datasets across different model classes has not been widely explored. This paper evaluates the transferability of satellite-derived inputs to eleven types of models, including process-based (SWAT), data-driven methods (XGBoost and WGAN), and hybrid model structures that utilize SWAT outputs with AI models. SHAP has been applied to overcome the black-box limitations of AI models and gain insights into fundamental hydrometeorological processes. In addition, uncertainty analysis was performed for all models, enabling a more comprehensive evaluation of performance. The results indicate that hybrid models using SWAT combined with WGAN can achieve better predictive accuracy than the SWAT model based on ground observation. While the baseline SWAT model achieved satisfactory performance during the validation period (NSE ≈ 0.86, KGE ≈ 0.80), the hybrid SWAT + WGAN framework improved simulation skill, reaching NSE ≈ 0.90 and KGE ≈ 0.89 during validation. Models forced with satellite-derived meteorological inputs additionally performed as well as those forced using station-based observations, validating the feasibility of using satellite products as alternative data sources. The future hydrological status of the basin was assessed based on the best-performing hybrid model and CMIP6 climate projections, showing a clear drought signal in the flows and long-term reductions in average flows reaching up to 58%. Overall, the findings indicate that the proposed framework provides a consistent approach for data-scarce basins. Future applications may benefit from integrating spatio-temporal learning frameworks and ensemble-based uncertainty quantification to enhance robustness under changing climate conditions. Full article

The causes of agricultural drought are complex, and its actual occurrence process is often characterized by rapid onset in terms of time and small scale in terms of space. Monitoring agricultural drought using satellite remote sensing with low spatial resolution makes it difficult to accurately capture the details of small-scale drought events. High-resolution satellite remote sensing has relatively long revisit cycles, making it difficult to capture the rapid evolution of drought conditions. Furthermore, the occurrence of agricultural drought is linked to multiple factors including precipitation, evapotranspiration, soil properties, and crop physiological characteristics. Consequently, relying on a single variable or indicator is insufficient for multidimensional monitoring of agricultural drought. This study takes Hebi City, Henan Province as the research area. It uses Sentinel-1 satellite data (HV, VV), Sentinel-2 data (NDVI, B2, B11), elevation, slope, aspect, and GPM precipitation data from 2019 to 2024 as independent variables. Three machine learning algorithms—Random Forest (RF), Random Forest-Recursive Feature Elimination (RF-RFE), and eXtreme Gradient Boosting (XGBoost)—were employed to construct a multi-dimensional agricultural drought monitoring model at the field scale. Additionally, the study verified the sensitivity of different environmental variables to agricultural drought monitoring and analyzed the accuracy performance of different machine learning algorithms in agricultural drought monitoring. The research results indicate that under the condition of full-factor input, all three models exhibit the optimal predictive performance. Among them, the XGBoost model performs the best, with the smallest Relative Root Mean Square Error (RRMSE) of 0.45 and the highest Correlation Coefficient (R) of 0.79. The absence of Digital Elevation Model (DEM) data impairs the models’ ability to capture the patterns of key features, which in turn leads to a reduction in predictive accuracy. Meanwhile, there is a significant correlation between model performance and sample size. Ultimately, the constructed XGBoost model takes the lead with an accuracy of 89%, while the accuracies of Random Forest (RF) and Random Forest-Recursive Feature Elimination (RF-RFE) are 88% and 86%, respectively. Based on these three drought monitoring models, this study further monitored a drought event that occurred in Hebi City in 2023, presented the spatiotemporal distribution of agricultural drought in Hebi City, and applied the Mann–Kendall test for time series analysis, aiming to identify the abrupt change process of agricultural drought. Meanwhile, on the basis of the research results, the feasibility of verifying drought occurrence using irrigation signals was discussed, and the potential reasons for the significantly lower drought occurrence probability in the western mountainous areas of the study region were analyzed. Full article

Sustainable food production for a growing population requires farming practices that reduce chemical inputs while maintaining soil as a living, renewable foundation for productivity. This review synthesizes current advances in understanding how endophytic fungi (EFs) interact with the soil microbiome and contribute to the physicochemical and biological dimensions of soil health in arable ecosystems. We examine evidence showing that EFs enhance plant nutrition through phosphate solubilization, siderophore-mediated micronutrient acquisition, and improved nitrogen use efficiency while also modulating plant hormones and stress-responsive pathways. EFs further increase crop resilience to drought, salinity, and heat; suppress pathogens; and influence key soil properties including aggregation, organic matter turnover, and microbial network stability. Recent integration of multi-omics, metabolomics, and community-level analyses has shifted the field from descriptive surveys toward mechanistic insight, revealing how EFs regulate nutrient cycling and remodel rhizosphere communities toward disease-suppressive and nutrient-efficient states. A central contribution of this review is the linkage of EF-mediated plant functions with soil microbiome dynamics and soil structural processes framed within a translational pipeline encompassing strain selection, formulation, delivery, and field scale monitoring. We also highlight current challenges, including context-dependent performance, competition with native microbiota, and formulation and deployment constraints that limit consistent outcomes under field conditions. By bridging microbial ecology with agronomy, this review positions EFs as biocontrol agents, biofertilizers, and ecosystem engineers with strong potential for resilient, low-input, and climate-adaptive cropping systems. Full article

Availability of irrigation water during growing seasons in the Republic of South Africa (RSA) remains a significant concern. Persistent droughts and unpredictable rainfall patterns attributed to climate change, coupled with an increasing population, have exacerbated irrigation water scarcity. Globally, treated wastewater has been utilised as an irrigation water source; however, despite global advances in the usage of treated wastewater, its suitability for irrigation in RSA remains a contentious issue. Considering this uncertainty, this review article aims to unravel the South African scenario on the suitability of treated wastewater for irrigation purposes and highlights the potential environmental impacts and public health risks. The review synthesised literature in the last two decades (2000–present) using Web of Science, ScienceDirect, ResearchGate, and Google Scholar databases. Findings reveal that treated wastewater can serve as a viable irrigation source in the country, enhancing various soil parameters, including nutritional pool, organic carbon, and fertility status. However, elevated levels of salts, heavy metals, and microplastics in treated wastewater resulting from insufficient treatment of wastewater processes may present significant challenges. These contaminants might induce saline conditions and increase heavy metals and microplastics in soil systems and water bodies, thereby posing a threat to public health and potentially causing ecological risks. Based on the reviewed literature, irrigation with treated wastewater should be implemented on a localised and pilot basis. This review aims to influence policy-making decisions regarding wastewater treatment plant structure and management. Stricter monitoring and compliance policies, revision of irrigation water standards to include emerging contaminants such as microplastics, and intensive investment in wastewater treatment plants in the country are recommended. With improved policies, management, and treatment efficiency, treated wastewater can be a dependable, sustainable, and practical irrigation water source in the country with minimal public health risks. Full article

Graphene oxide (GO) is a carbon-based nanomaterial explored for agricultural and forestry uses, but plant responses are strongly subject to both the dose and the route of exposure. We summarized recent studies with defined graphene oxide (GO) exposures by seed priming, foliar delivery, and root or soil exposure, while comparing annual crops with woody forest plants. Mechanistic progress points to a shared physicochemical basis: surface oxygen groups and sheet geometry reshape water and ion microenvironments at the soil–seed and soil–rhizosphere interfaces, and many reported shifts in antioxidant enzymes and hormone pathways likely represent downstream stress responses. In crops, low-to-moderate doses most consistently improve germination, root architecture, and tolerance to salinity or drought stress, whereas high doses or prolonged root exposure can cause root surface coating, oxidative injury, and photosynthetic inhibition. In forest plants, evidence remains limited and often relies on seedlings or tissue culture. For forest plants with long life cycles, processes such as soil persistence, aging, and multi-seasonal carry-over become key factors, especially in nurseries and restoration substrates. The available data indicate predominant root retention with generally limited root-to-shoot translocation, so residues in edible and medicinal organs remain insufficiently quantified under realistic-use patterns. This review provides a scenario-based framework for crop- and forestry-specific safe-dose windows and proposes standardized endpoints for long-term fate and ecological risk assessment. Full article

Lateral Organ Boundaries Domain (LBD) proteins are plant-specific transcription factors characterized by a typical N-terminal LOB domain and are critical for plant growth, development, and stress response. Currently, LBD genes have been investigated in various plant species, but they have yet to be identified in Neolamarckia cadamba, known as a ‘miracle tree’ for its fast growth and acknowledged for its potential medicinal value in tropical and subtropical areas of Asia. In this study, a total of 65 NcLBD members were identified in N. cadamba by whole-genome bioinformatics analysis. Phylogenetic analysis revealed their classification into two clades with seven distinct groups, and their uneven distribution across 18 chromosomes, along with 6 tandem repeats and 58 segmental duplications. Furthermore, enrichment analysis of transcription factor binding motifs within NcLBD promoters identified the MYB-related and WRKY families exhibited the most significant enrichment in the NcLBD promoter. Protein interaction network analysis revealed potential interactions among NcLBD proteins, as well as their interactions with various transcription factors. RNA-seq and qRT-PCR analyses of NcLBDs transcript levels showed distinct expression patterns both across various tissues and under different hormone and abiotic stress conditions. Specifically, NcLBD3, NcLBD37, and NcLBD47 were highly expressed in vascular cells and induced by abiotic stress, including cold, drought, and salt, suggesting their significant role in the processes. In summary, our genome-wide analysis comprehensively identified and characterized LBD gene family in N. cadamba, laying a solid foundation for further elucidating the biological functions of NcLBD genes. Full article

To address the issue of drought level confusion in the detection of drought stress during the seedling stage of the Yunnan large-leaf tea variety using the traditional YOLOv13 network, this study proposes an improved version of the network, MC-YOLOv13-L, based on animal vision. With the compound eye’s parallel sampling mechanism at its core, Compound-Eye Apposition Concatenation optimization is applied in both the training and inference stages. Simulating the environmental information acquisition and integration mechanism of primates’ “multi-scale parallelism—global modulation—long-range integration,” multi-scale linear attention is used to optimize the network. Simulating the retinal wide-field lateral inhibition and cortical selective convergence mechanisms, CMUNeXt is used to optimize the network’s backbone. To further improve the localization accuracy of drought stress detection and accelerate model convergence, a dynamic attention process simulating peripheral search, saccadic focus, and central fovea refinement in primates is used. Inner-IoU is applied for targeted improvement of the loss function. The testing results from the drought stress dataset (324 original images, 4212 images after data augmentation) indicate that, in the training set, the Box Loss, Cls Loss, and DFL Loss of the MC-YOLOv13-L network decreased by 5.08%, 3.13%, and 4.85%, respectively, compared to the YOLOv13 network. In the validation set, these losses decreased by 2.82%, 7.32%, and 3.51%, respectively. On the whole, the improved MC-YOLOv13-L improves the accuracy, recall rate and mAP@50 by 4.64%, 6.93% and 4.2%, respectively, on the basis of only sacrificing 0.63 FPS. External validation results from the Laobanzhang base in Xishuangbanna, Yunnan Province, indicate that the MC-YOLOv13-L network can quickly and accurately capture the drought stress response of tea plants under mild drought conditions. This lays a solid foundation for the intelligence-driven development of the tea production sector and, to some extent, promotes the application of bio-inspired computing in complex ecosystems. Full article

Silicon (Si) plays a critical role in regulating plant physiological processes, particularly through its influence on non-enzymatic antioxidant systems and amino acid metabolism. This study aims to assess soybean performance in response to both soil and foliar Si applications under well-watered and drought conditions, with the goal of enhancing Si accumulation in plant tissues and potentially strengthening the crop’s physiological responses to water deficit stress. This is especially pertinent given that the mechanisms underlying Si fertilization and its contribution to drought tolerance in soybean remain poorly understood. Greenhouse experiments were conducted using a 3 × 2 factorial design. The factors were: (i) three foliar Si treatments: control (no Si), potassium silicate (SiK; 128 g L⁻¹ Si, 126.5 g L⁻¹ K₂O, pH 12.0), and sorbitol-stabilized potassium silicate (SiKe; 107 g L⁻¹ Si, 28.4 g L⁻¹ K₂O, 100 mL L⁻¹ sorbitol, pH 11.8); and (ii) two soil water levels: well-watered (80% field capacity) and water-restricted (40% field capacity), the latter simulating tropical dry spells. Silicon was applied to the soil via irrigation and to the leaves via foliar spraying prior to the onset water restriction. All Si solutions were adjusted to pH 7.0 with 1 M HCl immediately before application. Potassium (K) levels were standardized across treatments through supplementary applications of KCl to both soil and foliage. Biometric and physiological parameters were subsequently measured. Sorbitol-stabilized Si enhanced Si accumulation in soybean tissues and improved plant resilience under both well-watered and drought conditions by promoting key physiological traits, including increased levels of daidzein and ascorbic acid levels, along with reduced amino acid concentrations. It also improved biometric parameters such as leaf area, root development, and number of pods per plant. These findings further support the role of Si as a beneficial element in enhancing stress tolerance and contributing to sustainable agricultural practices. Full article

Drought is a pervasive and intensifying climate hazard with profound implications for food security, water availability, and socioeconomic stability, particularly in sub-Saharan Africa. In Kenya, where over 80% of the landmass comprises arid and semi-arid lands (ASALs), recurrent droughts have become a critical threat to agricultural productivity and climate resilience. This study presents a comprehensive spatiotemporal analysis of seasonal drought dynamics in Kenya for June–July–August–September (JJAS) from 2000 to 2024, leveraging remote sensing-based drought indices and geospatial analysis for climate risk planning. Using the Standardized Precipitation Evapotranspiration Index (SPEI), Vegetation Condition Index (VCI), Soil Moisture Anomaly (SMA), and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) anomaly, a Combined Drought Indicator (CDI) was developed to assess drought severity, persistence, and impact across Kenya’s four climatological seasons. Data were processed using Google Earth Engine and visualized through GIS platforms to produce high-resolution drought maps disaggregated by county and land-use class. The results revealed a marked intensification of drought conditions, with Alert and Warning classifications expanding significantly in ASALs, particularly in Garissa, Kitui, Marsabit, and Tana River. The drought persistence analysis revealed chronic exposure in drought conditions in northeastern and southeastern counties, while cropland exposure increased by over 100% while rangeland vulnerability rose nearly 56-fold. Population exposure to drought also rose sharply, underscoring the socioeconomic risks associated with climate-induced water stress. The study provides an operational framework for integrating remote sensing into early warning systems and policy planning, aligning with global climate adaptation goals and national resilience strategies. The findings advocate for proactive, data-driven drought management and localized adaptation interventions in Kenya’s most vulnerable regions. Full article