Search Results (606)

Wheat (Triticum aestivum L.) is a strategic food crop for arid, hot regions such as the Arabian Peninsula, the Middle East, and North Africa. In these areas, production is limited by extreme environmental and agronomic conditions, leading to heavy dependence on imported wheat. Irrigation is often essential for successful cultivation, but available water sources are frequently saline. This study evaluated the comparative effects of irrigation salinity and genotype on agronomic performance, physiological responses, and grain quality. Nine Syrian wheat genotypes and one French bread-making cultivar, Florence Aurora, were grown in sandy soil under three irrigation salinity levels (2.6, 10, and 15 dS m⁻¹) across two seasons at the International Center for Biosaline Agriculture (Dubai, UAE). Salinity strongly negatively impacted yield, which decreased by 61% from the control to 15 dS m⁻¹, along with key yield components such as thousand grain weight and total biomass. Physiological traits, including carbon isotope composition (δ¹³C) and Na concentrations in roots, shoots and grains, increased significantly with salinity, while chlorophyll content showed a modest decline. Effects on grain quality were relatively minor: total nitrogen concentration and most mineral levels increased slightly, mainly due to a passive concentration effect associated with reduced TGW. Genotypes varied significantly in yield, biomass, TGW, physiological traits, and grain quality. The highest-yielding genotypes under control conditions (ACSAD 981 and ACSAD 1147) also performed best under saline conditions, and no trade-off was observed between yield and grain quality parameters (TGW, nitrogen, zinc, and iron concentrations). Separate analyses conducted for control and saline treatments identified different drivers of genotypic variability. Under control conditions, chlorophyll content, closely linked with δ¹³C, was the best predictor of genotypic differences and was positively correlated with yield across genotypes. Under salinity stress, grain magnesium (Mg) concentration was the strongest predictor, followed by grain δ¹³C, with both traits positively correlated with yield. These findings highlight key physiological traits linked to salinity tolerance and offer insights into the mechanisms underlying genotypic variability under both optimal and saline irrigation conditions. Full article

This study presents a comprehensive geospatial framework for assessing coastal vulnerability and ecosystem service distribution along the Greek coastline, one of the longest and most diverse in Europe. The framework integrates two complementary components: a Coastal Erosion Vulnerability Index applied to all identified beach units, and Coastal Flood Risk Indexes focused on low-lying and urbanized coastal segments. Both indices draw on harmonized, open-access European datasets to represent environmental, geomorphological, and socio-economic dimensions of risk. The Coastal Erosion Vulnerability Index is developed through a multi-criteria approach that combines indicators of physical erodibility, such as historical shoreline retreat, projected erosion under climate change, offshore wave power, and the cover of seagrass meadows, with socio-economic exposure metrics, including land use composition, population density, and beach-based recreational values. Inclusive accessibility for wheelchair users is also integrated to highlight equity-relevant aspects of coastal services. The Coastal Flood Risk Indexes identify flood-prone areas by simulating inundation through a novel point-based, computationally efficient geospatial method, which propagates water inland from coastal entry points using Extreme Sea Level (ESL) projections for future scenarios, overcoming the limitations of static ‘bathtub’ approaches. Together, the indices offer a spatially explicit, scalable framework to inform coastal zone management, climate adaptation planning, and the prioritization of nature-based solutions. By integrating vulnerability mapping with ecosystem service valuation, the framework supports evidence-based decision-making while aligning with key European policy goals for resilience and sustainable coastal development. Full article

The two-spotted spider mite, Tetranychus urticae Koch, is a major agricultural pest whose control is increasingly constrained by resistance to synthetic acaricides. This study evaluated the long-term field efficacy of three commercial entomopathogenic fungal (EPF) biopesticides—Velifer^® (Beauveria bassiana), Metab^® (B. bassiana + Metarhizium anisopliae), and Botanigard^® (B. bassiana)—against larval and protonymph stages of T. urticae on two host plants, Italian cypress (Cupressus sempervirens) and sweet orange (Citrus sinensis). Two foliar applications were conducted during the 2023 growing season (25 May and 25 July), and mite populations were monitored for 140 days after the final application. A randomized complete block design was used, and efficacy was calculated using the Henderson–Tilton formula. All EPF treatments significantly reduced mite populations compared with the untreated control throughout the monitoring period. Velifer consistently achieved the highest suppression of larval populations, particularly on C. sinensis, with efficacy comparable to the chemical standard. Botanigard showed more gradual but sustained population reduction over time, whereas Metab exhibited lower but stable efficacy in all cases. Treatment performance was strongly influenced by host plant species and mite developmental stage, with larvae consistently more susceptible than protonymphs. On C. sinensis, Velifer achieved the highest larval suppression (84.6%), comparable to the chemical standard abamectin, while Botanigard and Velifer were most effective on C. sempervirens. Survival analysis confirmed isolate- and host-dependent differences in hazard effects over time. These results demonstrate that EPF-based products can provide sustained, long-term suppression of T. urticae under field conditions, supporting their integration into integrated pest management programs. Full article

Potatoes intended for chip production must meet strict quality requirements. The objective of the study was to determine the optimal cultivation approach most favorable for chip potato cultivars (Beo, Picus, Pirol) through the application of various agronomic treatments, including a biostimulant and a fungicide. In the fresh tuber mass, the following components were determined: dry matter, starch, total and reducing sugars, as well as carotenoid and chlorophyll pigments. The chips were evaluated in terms of organoleptic traits: color, taste, aroma and consistency. All analyses were carried out directly after harvest and after 6 months of storage under constant temperature (8 °C) and relative air humidity (95%). In general, all experimental factors had a significant effect on the parameters studied. The potato cultivars differed significantly in the chemical composition of their tubers. The cultivar ‘Beo’ was characterized by the highest dry matter and starch content and, at the same time, the lowest content of total and reducing sugars (respectively, : 23.9%, 18.4%, 5.77 g kg⁻¹ f.m., 459 mg kg⁻¹ f.m.). The cultivar ‘Pirol’, on the other hand, contained the highest amounts of carotenoid and chlorophyll pigments (a, b and total): 10.31, 1.87, 0.927, 2.80 mg kg⁻¹ f.m., respectively. The preparations Moncut 460 SC (MC) and Supporter^® (SP) used in potato production showed a positive effect on the chemical composition of the cultivars studied. It was demonstrated that the combined use of both agents proved to be the most beneficial in this regard. The chips produced were characterized by high overall quality, averaging 4.6 points after harvest and 4.5 points after storage, fully meeting the standards required for this type of product. Chips fried from the tubers of the ‘Beo’ cultivar received the highest organoleptic scores: color—4.9, consistency—4.6, and taste—4.6 points. Regardless of the experimental factors, the chips were characterized by a very good aroma (5.0 points). The studies conducted generally demonstrated a positive effect of the potato seed treatments used in cultivation on the individual quality traits of the chips. The combined application of the preparations (MC and SP) generally had a significantly positive effect on the organoleptic characteristics of the chips. After long-term storage, the quality of tubers and chips slightly decreased overall, which indicates that appropriate conditions were maintained throughout the storage period and that proper handling of the tubers immediately after harvest was ensured. Full article

Arsenic contamination demands innovative, sustainable remediation. This study presents a fuzzy approach for synthesizing a magnetic biochar nanocomposite from pecan shell agricultural waste for efficient arsenic removal. Using a Multi-Input Fuzzy Rules Emulated Network (MiFREN), a systematic investigation of the synthesis process revealed that precursor type (biochar), Fe:precursor ratio (1:1), and iron salt type were the most significant parameters governing material crystallinity and adsorption performance, while particle size and N₂ atmosphere had a minimal effect. The MiFREN-identified optimal material, the magnetic biochar composite (FS7), achieved > 90% arsenic removal, outperforming the least efficient sample by 50.61%. Kinetic analysis confirmed chemisorption on a heterogeneous surface (q_e = 12.74 mg/g). Regeneration studies using 0.1 M NaOH demonstrated high stability, with FS7 retaining > 70% removal capacity over six cycles. Desorption occurs via ion exchange and electrostatic repulsion, with post-use analysis confirming structural integrity and resistance to oxidation. Application to real groundwater from the La Laguna region proved highly effective; FS7 maintained selectivity despite competing ions like ${Na}^{+}$, ${Cl}^{-},$ and ${SO}_4^{2-}$. By integrating AI-driven optimization with reusability and real contaminated water, this research establishes a scalable framework for transforming agricultural waste into a high-performance adsorbent, supporting global Clean Water and Sanitation goals. Full article

Reliable estimation of oil palm carbon stock is essential for climate mitigation, concession management, and sustainability certification. While satellite-based approaches offer scalable solutions, redundancy among spectral indices and inter-sensor variability complicate model development. This study evaluates machine learning regressors for predicting oil palm carbon stock at tree (CO_tree, kg C tree⁻¹) and hectare (CO_ha, Mg C ha⁻¹) scales using spectral indices derived from Landsat-8, Landsat-9, and Sentinel-2. Fourteen vegetation indices were screened for multicollinearity, resulting in a lean feature set dominated by NDMI, EVI, MSI, NDWI, and sensor-specific indices such as NBR2 and ARVI. Ten regression algorithms were benchmarked through cross-validation. Ensemble models, particularly Random Forest, Gradient Boosting, and XGBoost, outperformed linear and kernel methods, achieving R² values of 0.86–0.88 and RMSE of 59–64 kg tree⁻¹ or 8–9 Mg ha⁻¹. Feature importance analysis consistently identified NDMI as the strongest predictor of standing carbon. Spatial predictions showed stable carbon patterns across sensors, with CO_tree ranging from 200–500 kg C tree⁻¹ and CO_ha from 20–70 Mg C ha⁻¹, consistent with published values for mature plantations. The study demonstrates that ensemble learning with sensor-specific index sets provides accurate, dual-scale carbon monitoring for oil palm. Limitations include geographic scope, dependence on allometric equations, and omission of belowground carbon. Future work should integrate age dynamics, multi-year composites, and deep learning approaches for operational carbon accounting. Full article

In recent years, the quality of genome assemblies has notably improved, primarily due to advances in third-generation sequencing technologies and bioinformatics tools. In the present study, we obtained genome assemblies for two flax (Linum usitatissimum L.) varieties, K-3018 and Svyatogor, using Oxford Nanopore Technologies (ONT) simplex R10.4.1 data and the Hifiasm algorithm optimized for ONT reads. The K-3018 genome assembly was 491.1 Mb and consisted of thirteen full-length chromosomes and two one-gap chromosomes. The Svyatogor genome assembly was 497.8 Mb and consisted of twelve full-length chromosomes and three one-gap chromosomes. All chromosomes had telomeric repeats at their ends for both varieties. Hi-C contact maps and Illumina genomic data supported the accuracy of the obtained assemblies. The K-3018 and Svyatogor genome assemblies surpassed the quality of the best currently available flax genome assembly of variety T397, which serves as a reference for L. usitatissimum in the NCBI Genome database. Comparative analysis revealed that the flax genomes are generally quite similar at the chromosome level, with only a few large-scale differences. Thus, two near-T2T (telomere-to-telomere) flax genomes were assembled from the ONT simplex R10.4.1 reads using Hifiasm ONT without involving Pacific Biosciences (PacBio) HiFi or ultra-long ONT reads as well as optical maps. High-quality flax genomes are essential for improving the efficiency of genetic research, evaluating genetic diversity at the whole-genome level, and developing breeding and genome editing approaches of this valuable multipurpose crop. Full article

Cotton (Gossypium hirsutum L.) is a primary global commercial crop, and accurate monitoring of its growth and yield prediction are essential for optimizing water management. This study integrates leaf area index (LAI) data derived from unmanned aerial system (UAS) imagery into the Decision Support System for Agrotechnology Transfer (DSSAT) model to improve cotton growth simulation and yield estimation. The results show that the normalized difference vegetation index (NDVI) exhibited higher estimation accuracy for the cotton LAI during the squaring stage (R² = 0.56, p < 0.05), whereas the modified triangle vegetation index (MTVI) and enhanced vegetation index (EVI) demonstrated higher and more stable accuracy in the flowering and boll-setting stages (R² = 0.64 and R² = 0.76, p < 0.05). After assimilating LAI data, the optimized DSSAT model accurately represented canopy development and yield variation under different irrigation levels. Compared with the DSSAT, the assimilated model reduced yield prediction error from 40–52% to 3.6–6.3% under 30%, 60%, and 90% irrigation. These findings demonstrate that integrating UAS-derived LAI data with the DSSAT substantially enhances model accuracy and robustness, providing an effective approach for precision irrigation and sustainable cotton management. Full article

Effective municipal waste management is fundamental to environmental sustainability and the circular economy. This case study assesses the operational effectiveness of the Recycling/Civic Amenity Site (CAS) network in Białostocki county, Poland, during the 2014–2018 national waste management transition. A multi-criteria assessment was employed, integrating compliance audits, infrastructure checks, and spatial analysis of waste type distributions to evaluate CAS operations. The findings reveal a socio-economic divergence between more urbanised (town-and-village) and purely rural (village) municipalities, which is directly reflected in their distinct waste composition patterns. The town-and-village areas produced homogeneous, high-quality packaging waste streams that support recycling goals. Conversely, the village municipalities generated more commingled, heterogeneous streams that challenge recycling efforts. An optimised CAS model was proposed for the county to enhance sustainability by adaptively differentiating CAS services to local needs. However, a direct stock-take of all 16 CASs revealed significant infrastructural disparities, limiting the model’s potential. The study concludes that overcoming both the qualitative waste stream divergence and quantitative infrastructure disparities through tailored strategies is essential for meeting national recycling targets and achieving long-term sustainability. The methodology provides a replicable framework for pinpointing the root causes of inefficient operations, offering local authorities evidence-based tools to optimise CAS design and ensure infrastructure investments directly support overarching sustainability goals. Full article

In arid regions characterized by extreme water scarcity, it is important to synergistically optimize both crop yield and water use. Irrigation strategies based on empirical knowledge overlook crops’ dynamic water needs and may cause water waste and yield loss. To address this issue, this paper proposes an intelligent irrigation scheduling method based on a crop growth model and an improved deep reinforcement learning (DRL) agent. We construct a high-fidelity cotton growth environment using the Decision Support System for Agrotechnology Transfer (DSSAT) model. The model was calibrated with local data from the Shihezi region, Xinjiang, to provide a reliable simulation platform for DRL agent training. We developed a temporal state representation module based on a Bidirectional Long Short-Term Memory (BiLSTM) network and an attention mechanism. This module captures dynamic trends in historical environmental information to focus on critical decision factors. The Soft Actor–Critic (SAC) algorithm was improved by integrating a feature attention mechanism to enhance decision-making precision. A dynamic reward function was designed based on the critical growth stages of cotton to incorporate agronomic prior knowledge into the optimization objective. Simulation results demonstrate that our proposed method can improve water use efficiency (WUE) by 39.0% (with an 8.4% increase in yield and a 22.1% reduction in water consumption) compared to fixed-schedule irrigation strategies. An ablation study further confirms that each of our proposed modules—BiLSTM, the attention mechanism, and the dynamic reward—makes a significant contribution to the final performance. Full article

Ports are vital components of global and regional supply chains, supporting trade, transport connectivity, and socio-economic development. However, their functionality is increasingly threatened by climatic hazards such as sea-level rise and heat stress, both of which are projected to intensify under future climate change. This study presents a comprehensive framework for assessing the criticality of ports within a national network, demonstrated through its application to the Greek port system, which encompasses a multitude of ports of all types from large international hubs to small island ones. The framework combines openly accessible geospatial and socio-economic data with projections of exposure to sea-level rise and extreme heat within a structured multi-criteria decision-making (MCDM) approach, enabling the identification of critical ports and the prioritization of adaptation needs. Results show that large mainland ports dominate in socio-economic importance and network centrality, while smaller island ports are vital locally due to limited redundancy and high exposure to climatic hazards. By 2100, nearly all ports are projected to experience freeboard reductions below operational thresholds and increased heat-related stress. These results highlight the need for targeted adaptation measures, including engineering interventions for mainland ports and redundancy-enhancing actions for island ports. The proposed framework provides a replicable, data-driven tool to guide evidence-based prioritization of adaptation investments and strengthen climate-resilient maritime transport and coastal management, thereby contributing to the achievement of Sustainable Development Goals (SDGs) 1.5, 9 and 13. Full article

Ischemic heart disease (IHD) remains the predominant cause of global mortality, necessitating accurate incidence forecasting for effective prevention strategies. Existing statistical models inadequately capture nonlinear epidemiological patterns, while deep learning approaches lack clinical interpretability. We constructed an interpretable predictive framework combining particle swarm optimization (PSO), bidirectional long short-term memory (BiLSTM) networks, and a novel multi-scale attention mechanism. Age-standardized incidence rates (ASIRs) from the Global Burden of Disease (GBD) 2021 database (1990–2021) were stratified across 24 sex-age subgroups and processed through 10-year sliding windows with advanced feature engineering. SHapley Additive exPlanations (SHAP) provided a three-level interpretability analysis (global, local, and component). The framework achieved superior performance metrics: mean absolute error (MAE) of 0.0164, root mean squared error (RMSE) of 0.0206, and R² of 0.97, demonstrating a 93.96% MAE reduction compared to ARIMA models and a 75.99% improvement over CNN–BiLSTM architectures. SHAP analysis identified females aged 60–64 years and males aged 85–89 years as primary predictive contributors. Architectural analysis revealed the residual connection captured 71.0% of the predictive contribution (main trends), while the BiLSTM–Attention pathway captured 29.0% (complex nonlinear patterns). This interpretable framework transforms opaque algorithms into transparent systems, providing precise epidemiological evidence for public health policy, resource allocation, and targeted intervention strategies for high-risk populations. Full article

The implementation of immersion heaters in hydroponic strawberry systems offers substantial potential for reducing glasshouse operational costs. This 115-day study investigated the effects of nutrient solution temperature on strawberry physiological and biochemical parameters. Temperature significantly influenced anthocyanin accumulation, with a maximum increase (135.49%) at 20 °C. Total chlorophyll content and photosystem II efficiency (Fv/Fm) exhibited temperature-dependent variations, while the 20 °C treatment served as the optimal baseline. Plants maintained at 20 °C demonstrated superior growth performance, achieving 64.79% higher fresh shoot weight and 50.29% greater total dry biomass compared to controls. Fruit quality parameters remained largely temperature-independent, except at 15 °C, which produced fruits with elevated sugar content but reduced acidity and dimensions. Conversely, the 20 °C treatment yielded the maximum fruit weight. Photosynthetic rates peaked during the experimental period, with plants at 20 °C exhibiting optimal recovery capacity. Both transpiration and stomatal conductance displayed treatment-specific patterns, with 20 °C maintaining superior physiological responses despite stress periods. These findings establish that maintaining nutrient solution temperature at 20 °C optimizes strawberry physiology, growth, and fruit quality, validating temperature regulation as an effective practice for hydroponic strawberry production systems. Full article

Background: The use of baobab seed beverages as coffee alternatives represents a novel approach to upcycling by-products. Baobab seed aqueous extract is caffeine-free and contains numerous compounds of nutritional interest. The composition and sensory characteristics of baobab seed beverage can be modulated by roasting and brewing conditions. Objective: This study aimed to assess the effect of using different fluidised bed roasting temperatures and microwave infusion on the nutritional and functional properties of the beverage. Results: Higher roasting temperatures increased solubility, melanoidin content, pH, titratable acidity, colour, phenolic content, and antioxidant activity, while the concentration of chlorogenic acid and caffeic acid decreased. Upon microwave infusion, antioxidant activity, phenolic content (gallic acid, coumaric acid, caffeic acid, and vanillic acid), protein content, and soluble fibre content increased. Chlorogenic acid was not present in microwave-infused samples, and the amount of caffeic acid decreased. The fat content remained similar across all samples. The major volatile components identified in the roasted seeds were furans and pyrazines. Conclusions: These findings highlight the potential of baobab seed beverages as coffee alternatives and the impact of roasting and brewing conditions on their nutritional and functional properties. Full article

The citrus spiny whitefly Aleurocanthus spiniferus (Quaintance), recently found in Greece, causes severe damage to the leaves and fruits of tree crops, and treatment against it is urgent. In this work, integrated treatments for the management of the A. spiniferus pest on Citrus sinensis (L.) trees, which causes intense damage to orange orchards, were studied. The experiment was carried out in an orange orchard on the Aitoloakarnania plain, an agroforestry ecosystem, and three treatments were set up: (i) a combined treatment comprising the entomopathogenic fungi Beauveria bassiana and Cordyceps fumosorosea, (ii) treatment with the application of a tetramic acid-based formulation, (iii) the control treatment. The damage caused by A. spiniferus was estimated by determining the pest stages on the C. sinensis leaves, samples of which were collected and examined at the entomology laboratory of the Agriculture Faculty of the University of Ioannina for the calculation of populations. The experimental results of this work encourage us to further investigate the use of the treatments against whiteflies, highlighting the potential of EPF for integrated pest management (IPM) in citrus trees. Full article