Search Results (615)

Malaysia’s aspiration to attain high-income status necessitates not only economic growth but also a deeper understanding of poverty that goes beyond financial indicators. The Multidimensional Poverty Index (MPI) for Malaysia is designed to be comprehensive, considering a wide range of factors relevant to the diverse population of the country. Unlike traditional income-based approaches, our study goes beyond money to capture how poverty affects households across multiple dimensions. The MPI reveals important insights that standard measures often miss—showing which families struggle with education, health, housing, or digital access, not just income. Therefore, this study aims to enhance the Multidimensional Poverty Index for the Malaysian context by identifying and incorporating new dimensions and indicators to better capture the complexity of poverty in the country based on an empirical study in Kuala Lumpur, Malaysia. The MPI represents a significant advancement, offering a multidimensional framework for poverty measurement. Based on results in Kuala Lumpur, 38.7% of households were found to be multidimensionally poor. This means that nearly 4 out of every 10 households in this study experienced deprivations in multiple basic needs, not just income. Household size also significantly influences the risk of multidimensional poverty, with households of more than six members being over three times more likely to be poor, primarily due to higher dependency ratios and greater consumption burdens. Full article

Chieng Hac in northern Vietnam is expanding maize cultivation, intensifying water competition and soil erosion. This study mapped regional water balance and erosion using remote sensing and GISs by coupling the Thornthwaite–Mather (TM) water balance model with the Revised Universal Soil Loss Equation (RUSLE) at 12.5 m resolution. Land cover was classified into maize, tree crops, paddy, forest, and other types using Random Forest. The TM model used 2021 precipitation and temperature measurements to estimate evapotranspiration, surplus, and deficit, while the RUSLE quantified soil loss. Two scenarios were evaluated: a baseline reflecting existing land use and an adjusted case applying strip cropping on 10–20° maize slopes and converting maize to tree crops on slopes > 20°. Tree crop conversion increased evapotranspiration and prolonged seasonal deficits relative to maize, increasing water deficit from 1013.6 to 1022.2 mm/year. In contrast, the interventions reduced mean soil loss from 15.52 to 11.51 t/ha/year, with the largest decline in the 5–25 t/ha/year class. Residual hotspots persisted on steep slopes and near drainage lines. The integrated framework highlights trade-offs between erosion control and seasonal water availability, supporting slope-based land-use planning in upland agricultural systems. These findings offer guidance for slope-based land-use planning by indicating that intervention priorities should vary depending on slope conditions and local water availability. Full article

The management of Culex pipiens (Diptera: Culicidae), key vectors of arboviruses like West Nile virus, necessitates sustainable alternatives to chemical insecticides. This study screened indigenous entomopathogenic fungi (EPF) from forest soils in Achaia, Greece, for their larvicidal efficacy against Cx. pipiens biotype molestus. Fifteen fungal isolates were obtained via insect baiting and identified as Beauveria and Metarhizium species. A comprehensive bioassay at 1 × 10⁸ conidia mL⁻¹ revealed significant variation in pathogenicity after 72 h. Two isolates, Beauveria bassiana (BB) (Hypocreales: Cordycipitaceae) and Metarhizium anisopliae (K3(1)) (Hypocreales: Clavicipitaceae), exhibited the highest virulence among the tested isolates, each causing 60% mortality with a rapid median lethal time (LT₅₀) of ~18.5 h. Survival analysis, Cox modeling, and non-linear kinetic modeling (Gompertz/Richards) classified three distinct virulence clusters: high/rapid, moderate/consistent, and low/delayed. A pathogenicity network analysis and a composite virulence index further validated BB and K3(1) as the most effective candidates. These results demonstrate the high isolate specificity of fungal efficacy and underscore the importance of screening local fungal diversity. The identified high-virulence isolates represent promising, environmentally sound candidates for the development of targeted biopesticides. Future research should focus on formulation for aquatic environments and integration into resistance-resilient integrated vector management programs. Full article

Low forage productivity of natural grasslands remains a major limitation for sustainable livestock production in the forest–steppe zone of Northern Kazakhstan, highlighting the need for high-yield, locally adapted forage systems. This study evaluated nine forage agrophytocenoses, including perennial grasses and legume–grass mixtures, established in 2024 and assessed over two growing seasons on leached chernozem soils. Plant height, stand density, and biomass yields were quantified at optimal harvest stages, with statistical differences tested using one-way ANOVA and Tukey’s HSD (p < 0.05). Legume-containing agrophytocenoses consistently outperformed natural grass cover and grass monocultures in canopy development and biomass accumulation. The highest productivity was achieved in Lolium multiflorum + Medicago sativa (I+A), Medicago sativa + Festuca arundinacea (A+TF), and Onobrychis viciifolia + Festulolium + Phleum pratense (S+F+T), reaching up to ~19.66 t ha⁻¹ green biomass and ~5.24 t ha⁻¹ dry matter. In contrast, Agropyron cristatum monoculture yielded minimally during establishment, while ryegrass mixtures with annuals declined in the second year. Optimized legume–grass agrophytocenoses represent the most productive and agronomically reliable strategy to enhance forage supply and improve environmental resilience in Northern Kazakhstan. Full article

Common buckwheat (Fagopyrum esculentum Mill.) is a pseudocereal that has recently gained increasing interest among both farmers and scientists. Its low soil requirements, high adaptability, and high resistance to diseases and pests allow it to be cultivated in many regions of the world. It is recommended for various cultivation systems, especially for low-input and organic farming. Currently, buckwheat is grown mainly for seeds and less often for green fodder. Thanks to its above-average nutritional value and many benefits that support human health, it is considered one of the leaders in functional food. It can be a basic raw material for many food products such as flour, groats, and flakes, but can also be used as a valuable addition to crisps, bars and drinks. Recently, buckwheat’s usefulness in the energy industry, construction, medicine, and pharmacology has been confirmed. Buckwheat, as a plant species distinct from the dominant global crops, fits very well into the current standards and assumptions of sustainable development. Its cultivation and consumption are associated with a number of benefits not only for human health but also for the whole environment. It is considered a species that counteracts climate change. Buckwheat’s valuable properties include its positive impact on soil physicochemical properties, its enhancement of biodiversity, and its support for pollinators. It is considered a species that can be cultivated in a changing climate, generating a very low carbon footprint. The aim of this study was to determine the contemporary economic importance of buckwheat, its place among species supporting sustainable development, and to identify potential research areas that will contribute to strengthening buckwheat’s role in sustainable agriculture. Full article

Peri-urban ecosystems represent underexplored habitats rich in entomopathogenic fungi (EPF) that can serve as valuable resources for managing insect pests. This study characterized the EPF communities in two peri-urban sites near Patras, Greece (Dasyllio and Elos), during 2018–2019. Soil samples were collected seasonally, and fungi were isolated using insect baiting with Tribolium confusum Jacquelin du Val and Sitophilus zeamais Motsch., a selective method favoring generalist, fast-acting entomopathogens. A total of 814 isolates were recovered. Of a randomly selected subset (n = 177) subjected to molecular identification, 46.9% were characterized as known EPF, while 53.1% were classified as putative EPF based on taxonomic affiliation (ITS sequence similarity ≥ 99%), pending confirmation of pathogenicity. The Dasyllio site yielded more isolates (63.4%) than Elos (36.6%). Seasonal trends strongly influenced EPF occurrence, with infective fungi peaking in spring and summer (p < 0.001), while community diversity remained stable throughout the year, with the highest evenness (Evenness Index = 0.93) observed in autumn. These results highlight peri-urban green spaces as reservoirs of diverse and ecologically stable EPF, suggesting their potential as sources of biocontrol agents for future development and seasonal integration into pest management strategies. Full article

Due to the complexity inherent in river ice dynamics, the utilization of remote sensing imagery represents the most crucial and effective method currently available for monitoring changes in river ice. In the Inner Mongolia segment of the Yellow River during winter, two distinct types of ice surfaces are observed: juxtaposed ice and consolidated ice. Additionally, certain areas of open water remain unfrozen. Rapid identification and classification of extensive ice formations and open water zones along this lengthy river section constitute critical information for informed decision-making in ice prevention and management strategies within the Yellow River basin. This paper takes the formation and characteristic analysis of different types of ice in the Yellow River channels in Inner Mongolia as the starting point. It employs a support vector machine (SVM) as the classifier and introduces an optimized model for classifying river ice types using high-resolution Sentinel-2 optical imagery. The model utilizes multi-band spectral features, along with multi-spectral fusion indices such as the normalized difference snow index (NDSI) and the normalized difference frozen surface index (NDFSI), as feature vectors. This approach attains an overall accuracy of 94.91% in classifying different types of ice and can significantly contribute to river ice monitoring by offering robust theoretical support. In the winter of 2023–2024, the proportion of juxtaposed ice on the Yellow River section in Inner Mongolia changed from 45% to 55%, the proportion of consolidated ice changed from 30% to 40%, and the proportion of open water changed from 9% to 19%. This research investigates the characteristics of river ice formations and develops a classification methodology for river ice patterns utilizing high-resolution Sentinel-2 imagery in conjunction with a supervised classification algorithm. The findings of this study are intended to offer technical support for the expedited interpretation of ice conditions in the Yellow River, thereby serving as a scientific basis for precise monitoring and effective disaster prevention and management related to river ice phenomena. Full article

Brucellosis continues to pose a substantial zoonotic risk in Kazakhstan; however, evidence describing the knowledge, attitudes, and practices (KAP) of cattle farmers and veterinary personnel remains limited. A cross-sectional study was undertaken between May and October 2024 across twelve administrative locations nationwide. Structured questionnaires were administered to 506 cattle farmers and 33 veterinary professionals, and the data were evaluated using descriptive analyses and univariable logistic regression. Awareness of brucellosis in cattle was relatively high among farmers, yet understanding of its implications for human health was markedly lower. In contrast, animal health workers demonstrated consistently higher levels of knowledge (OR: 12.6; 95% CI: 9.88–16.34; p < 0.001). Several practices associated with zoonotic transmission were commonly reported by farmers, including handling aborted materials without protective gloves, consumption of unpasteurised milk, and leaving reproductive tissues in grazing areas. Nevertheless, most farmers expressed readiness to adopt preventive measures, particularly cattle vaccination and the use of basic protective practices. These findings reveal important gaps between awareness and behavior that may contribute to continued transmission of brucellosis. Strengthening farmer education through locally tailored, One Health-based interventions offers a practical pathway to improving brucellosis control and enhancing cattle productivity in Kazakhstan. Full article

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