Search Results (4,821)

The genetic diversity of the olive tree (Olea europaea L.) is critical for enhancing crop resilience and productivity under changing climatic conditions. Peru’s southern region, particularly Tacna, hosts over 30 olive cultivars, yet their genetic structure remains poorly characterized. This study aimed to evaluate the morphological and genomic diversity of ten economically important olive varieties cultivated in 15 sectors across Tacna and Jorge Basadre provinces. A total of 92 mother plants were selected for morphological assessment using 25 standardized descriptors. Additionally, genomic DNA was extracted from 30 samples and subjected to genotyping-by-sequencing (GBS). Quality metrics confirmed the efficiency of a modified 6h-DNA extraction protocol. Bioinformatic analysis identified hundreds of thousands of SNPs per variety, with a high transition/transversion ratio (∼2.1), indicating reliable variant calls. Phylogenetic clustering revealed three diversity groups, with the olive cultivars Ascolana and Frantoio exhibiting high genetic variability, and Arbequina and Leccino—also olive cultivars—showing reduced diversity. The integration of phenotypic and genomic data highlights hidden variability and supports informed selection and conservation strategies. These findings provide a genomic baseline for breeding programs and genetic resource management in emerging olive-growing regions such as southern Peru. Full article

The Global Health Network Middle East and North Africa (TGHN MENA) was officially launched on 21 October 2024, representing a pivotal initiative to address the region’s distinct and complex public health challenges. Building on the comprehensive global framework of the central TGHN network, the regional TGHN MENA network was founded by region-based experts with support from the TGHN team. The network was established as a pioneering initiative to bring together 18 partners from 14 countries, representing various sectors such as academia, policymakers, and governmental and non-governmental organizations, to tackle pressing issues such as chronic diseases, mental health, and climate change impacts. High-level panel discussions were held to define the goals of TGHN MENA in building resilient public health systems. This perspective outlines the network’s vision for building resilient health systems through research prioritization and capacity strengthening, amidst growing uncertainties in the regional public health landscape. The MENA region has diverse and complex public health challenges related to health systems, emergencies, chronic disease, mental health disorders, and climate change, due to cultural, social, and geographic differences. The TGHN MENA network is a community of practice and can identify commonalities and priorities and find shareable solutions. Key strategies proposed include establishing an open-access, online platform to support knowledge exchange, implementing on-the-job training and capacity-strengthening initiatives, and emphasizing the use of artificial intelligence in public health research. This perspective outlines TGHN MENA’s inaugural one-year action plan, which emphasizes regular knowledge-sharing activities, capacity-building initiatives, and sustained partners’ commitment as foundational steps towards improved public health outcomes in the region. Full article

Forest fires critically disrupt soil ecosystems by altering physicochemical properties and microbial structure-function dynamics. This study assessed short-term impacts of fire intensities (light/moderate/heavy) on microbial communities in Larix gmelinii forests one year post-fire. Using phospholipid fatty acid (PLFA) and Biolog EcoPlate analyses, we found the following: (1) fire reduced soil organic carbon (SOC), dissolved organic carbon (DOC), total nitrogen (TN), and available nitrogen/potassium (AN/AK) via pyrolytic carbon release, while heavy-intensity fires enriched available phosphorus (AP), AN, and AK through ash deposition. (2) Thermal mortality and nutrient-pH-moisture stress persistently suppressed microbial biomass and metabolic activity. Moderate fires increased taxonomic richness but reduced functional diversity, confirming “functional redundancy.” (3) Neither soil microbial biomass nor metabolic activity at the fire site reached pre-fire levels after one year of recovery. Our findings advance post-fire soil restoration frameworks and advocate multi-omics integration to decode fire-adapted functional gene networks, guiding climate-resilient forest management. Full article

Transportation agencies face increasing challenges in identifying and prioritizing which infrastructure assets are most critical to maintain and protect, particularly amid aging networks, limited budgets, and growing threats from climate change and extreme events. However, existing prioritization approaches often lack consistency and fail to adequately incorporate diverse stakeholder perspectives. This study develops a systematic, stakeholder-informed method for ranking transportation assets based on their criticality to the overall transportation system. As a novel approach, we use the analytical hierarchy process (AHP) and present a case study of the applied approach. Six criteria were identified for ranking assets: annual average daily traffic (AADT), redundancy, freight output, roadway classification, Social Vulnerability Index (SoVI), and tourism. Stakeholder input was collected via an AHP-based survey using pairwise comparisons and translated into weighted rankings. Thirty complete responses (13.2% response rate) from experts (i.e., engineers, analysts, planners, etc.) were analyzed, with the resulting ranks from highest to lowest priority being AADT, redundancy, freight output, roadway classification, SoVI, and tourism. Stability analysis confirmed that rankings were consistent with a minimum of 15 responses. The resulting method provides a practical, replicable tool for agencies to perform statewide vulnerability/resiliency assessments ensuring that decision-making reflects a broad range of expert perspectives. Full article

Accurate information on species distributions and population sizes is essential for effective biodiversity conservation, yet such data are often lacking at national scales. This study addresses this gap by assessing the distribution and abundance of 111 bird species across Croatia, including breeding, wintering, and migratory flyway populations. We combined Species Distribution Models (SDMs) with expert-based population estimates to generate spatially explicit predictions. The modeling framework incorporated high-resolution Earth observation (EO) data and advanced spatial analysis techniques. Environmental variables, such as land cover, were derived from satellite datasets, while climate variables were interpolated from ground measurements and refined using EO-based co-variates. Model calibration and validation were based on species occurrence records and EO-derived predictors. This integrative approach enabled both national-scale population estimates and fine-scale habitat assessments. The results identified critical habitats, population hotspots, and areas likely to experience distribution shifts under changing environmental conditions. By integrating EO data with expert knowledge, this study enhances the robustness of population estimates, particularly where species monitoring data are incomplete. The findings support conservation prioritization, inform land use and resource management, and contribute to long-term biodiversity monitoring. The methodology is scalable and transferable, offering a practical framework for ecological assessments in diverse regions. We integrated expert-based population estimates with species distribution models (SDMs) by applying expert-derived density values to areas of suitable habitat predicted by SDMs. This approach enables spatially explicit population estimates by combining ecological modeling with expert knowledge, which is particularly useful in systems with limited data. Experts provided species-specific density estimates stratified by habitat type, seasonality, behavior, and detectability, aligned with habitat suitability classes derived from SDM outputs. Full article

Climate variability intensifies weather risks across smallholder rainfed farming systems in Africa. Farmers often respond by minimizing the use of modern inputs and opting for low-cost traditional practices, a strategy that decreases average yields and perpetuates poverty. While crop insurance could incentivize greater adoption of inputs, indemnity-based programs face market failures. Weather index insurance (WII), which utilizes objective weather data to trigger payouts while addressing traditional crop insurance market failures, is a viable solution. However, empirical evidence on the impact of WII remains limited, with most studies relying on controlled experiments or hypothetical scenarios that overlook real-world adoption dynamics. This study analyzed observational data from 400 smallholder farmers across diverse agroecological zones in Njoro Sub-County, Kenya, using instrumental variable regression to evaluate the impact of weather index insurance (WII) on input adoption and intensity of use. Findings indicated that WII significantly increased the adoption and intensification of improved inputs while displacing traditional practices, with effects moderated by gender, financial access, and infrastructure. Specifically, active WII users applied 28.7 kg/acre more chemical fertilizer and used 2.6 kg/acre more hybrid maize seeds while reducing manure and traditional seed usage by 27 kg/acre and 2.9 kg/acre, respectively. However, the effectiveness of WII was context-dependent, varying under extreme drought conditions and in high-fertility soils, which directly affected resilience outcomes. These findings suggest that policies should combine insurance with targeted agroecological practices and complementary measures, such as improved access to credit and gender-sensitive extension programs tailored to the specific needs of women farmers, to support sustainable agricultural transformation. Full article

Seed quality traits, such as seed size, oil and protein content, mineral accumulation, and morphological characteristics, are crucial for enhancing crop productivity, nutritional value, and marketability. Traditional quantitative trait loci (QTL) mapping methods, such as linkage analysis and genome-wide association studies (GWAS), have played fundamental role in identifying loci associated with these complex traits. However, these approaches often struggle with high-dimensional genomic data, polygenic inheritance, and genotype-by-environment (GXE) interactions. Recent advances in artificial intelligence (AI) and machine learning (ML) provide powerful alternatives that enable more accurate trait prediction, robust marker-trait associations, and efficient feature selection. This review presents an integrated overview of AI/ML applications in QTL mapping and seed trait prediction, highlighting key methodologies such as LASSO regression, Random Forest, Gradient Boosting, ElasticNet, and deep learning techniques including convolutional neural networks (CNNs) and graph neural networks (GNNs). A case study on soybean seed mineral nutrients accumulation illustrates the effectiveness of ML models in identifying significant SNPs on chromosomes 8, 9, and 14. LASSO and ElasticNet consistently achieved superior predictive accuracy compared to tree-based models. Beyond soybean, AI/ML methods have enhanced QTL detection in wheat, lettuce, rice, and cotton, supporting trait dissection across diverse crop species. I also explored AI-driven integration of multi-omics data—genomics, transcriptomics, metabolomics, and phenomics—to improve resolution in QTL mapping. While challenges remain in terms of model interpretability, biological validation, and computational scalability, ongoing developments in explainable AI, multi-view learning, and high-throughput phenotyping offer promising avenues. This review underscores the transformative potential of AI in accelerating genomic-assisted breeding and developing high-quality, climate-resilient crop varieties. Full article

Integrating sustainability into STEAM education is crucial for fostering environmental awareness among students. The Erasmus+ project Clean Environment–Clean School Climate with Creative Environmental Practices in School Education—Clean&Creative aims to develop environment-themed curriculum content that seamlessly integrates into ten different STEAM school disciplines. This initiative enhances multidisciplinary learning by connecting scientific knowledge with creative environmental practices, equipping students with the skills and mindset needed for sustainable problem solving. This paper presents the project’s key findings, highlighting innovative pedagogical approaches that merge sustainability with STEAM and humanities-based education. By incorporating hands-on, creative activities into school curricula, the project fosters active student engagement and a deeper understanding of environmental challenges. The results demonstrate how multidisciplinary strategies can bridge the gap between scientific principles and real-world sustainability issues, reinforcing the role of education in shaping eco-conscious citizens. Furthermore, the study discusses the challenges and opportunities in implementing these practices, providing insights into their long-term impact on students and educators. The findings contribute to the ongoing discourse on sustainability education, offering practical solutions for integrating environmental themes into diverse educational contexts. Ultimately, this research underscores the importance of creative, inter- and multidisciplinary methods in promoting sustainability within STEAM and humanistic education. Full article

This study explores the relationship between land use and land cover (LULC) changes and a significant cyclogenesis event that occurred in Alabama’s Black Belt region from 6 to 7 October 2021. Utilizing the Weather Research and Forecasting (WRF) model, two scenarios were analyzed: the WRF Control Run, which maintained unchanged LULC, and the WRF Sensitivity Experiment, which converted 56.5% of forested areas into cropland to assess the impact on storm dynamics. Quantitative comparisons of predicted rainfall from both simulations were conducted against observed data. The control run demonstrated a Root Mean Square Error (RMSE) of 1.64, indicating accurate rainfall predictions. In contrast, the modified scenario yielded an RMSE of 2.01, suggesting lower reliability. The Mean Bias (MB) values were 1.32 for the control run and 1.58 for the modified scenario, revealing notable discrepancies in accuracy. The coefficient of determination (R²) was 0.247 for the control run and 0.270 for the modified scenario. The Nash–Sutcliffe Efficiency (NSE) value was 0.1567 for the control run but dropped to −0.2257 following LULC modifications. Sensitivity analyses revealed a 60% increase in heat flux and a 36% rise in precipitation, underscoring the significant impact of LULC on meteorological outcomes. While this study concentrated on the Black Belt region, the methodologies employed could apply to various other areas, though caution is advised when generalizing these results to different climates and socio-economic contexts. Further research is necessary to enhance the model’s applicability across diverse environments. Full article

As global concerns about climate change and air quality intensify, nations are increasingly adopting sustainable transportation solutions, with electromobility emerging as a key alternative. This study investigates the factors influencing powertrain technology choice and the barriers to electric vehicle (EV) adoption in Poland, focusing on insights from technically educated youth, early-career researchers, and academic professionals. Drawing on a mixed-methods approach, the study investigates public perceptions, motivations, and challenges associated with EV uptake in a country historically reliant on fossil fuels. Key drivers such as environmental considerations, government policies, and infrastructure development are evaluated alongside persistent obstacles, including high initial purchase costs, inadequate charging networks, range anxiety, and scepticism about battery performance. While the sample is not representative of the broader Polish population, it provides insights from a technically literate cohort likely to shape future technological and policy advancements. Our findings reveal that the adoption of EVs among this group is influenced by factors such as technological innovation and government policies, while barriers include high initial costs, limited charging infrastructure, and scepticism about perceived sustainability, battery life, and performance. The research also highlights the critical role of education and awareness in shaping attitudes toward EVs. This study, though limited by sample size and demographic focus, offers valuable contributions to understanding the early-stage adoption of EVs in Poland and serves as a foundation for future research targeting a more diverse population. The applied research model is scalable, providing a framework for broader studies that could include different age groups, geographical regions, and professional sectors. Full article

Despite growing importance, agricultural landscapes face threats, like fragmentation, shrinkage, and degradation, due to climate change. Although remote sensing and GIS are widely used in monitoring croplands, integrating machine learning, remote sensing, GIS, and landscape metrics for the holistic management of this landscape remains underexplored. Thus, this study monitored crop patterns using random forest (94% accuracy), the role of geographical factors (such as elevation, aspect, slope, maximum and minimum temperature, rainfall, cation exchange capacity, NPK, soil pH, soil organic carbon, soil type, soil water content, proximity to drainage, proximity to market, proximity to road, population density, and profit per hectare production), diversity, combinations, and fragmentation using landscape metrics and a fragmentation index. Findings revealed that slope, rainfall, temperature, and profit per hectare production emerged as significant drivers in shaping crop patterns. However, anthropogenic drivers became deciding factors during spatial overlaps between crop suitability zones. Rice belts were the least fragmented and highly productive with a risk of monoculture. Croplands with a combination of soybean, black grams, and maize were highly fragmented, despite having high diversity with comparatively less production per field. These diverse fields were providing higher profits and low risks of crop failure due to the crop combinations. Equally, intercropping balanced the nutrient uptakes, making the practice sustainable. Thus, it can be suggested that productivity and diversity should be prioritized equally to achieve sustainable land use. The development of the PCA-weighted fragmentation index offers an efficient tool to measure fragmentation across similar agricultural regions, and the integrated approach provides a scalable framework for holistic management, sustainable land use planning, and precision agriculture. Full article

The subtropical grasslands of South America are known as Pampa, span parts of Brazil, Uruguay, and Argentina, and are undergoing rapid and alarming transformations due to agricultural expansion, habitat fragmentation, and climate change. Despite this, these areas harbor a remarkable diversity of Cactaceae, including a high proportion of endemic and threatened species. This study offers the first comprehensive inventory of native and endemic cactus taxa in the Pampean province of the Chacoan domain, integrating data from georeferenced herbarium records, biodiversity databases, and fieldwork. A total of 111 native taxa were identified, of which 62% are endemic to the region. Spatial analyses reveal that many species occur outside protected areas, with hotspots of richness and endemism located near international borders and in poorly studied regions. These findings underscore the urgent need to reassess conservation priorities in Pampa, where biodiversity is being lost at an accelerating pace. By identifying critical areas for conservation and highlighting gaps in species assessments, the present study contributes essential data to support public policy, conservation planning, and the establishment of cross-border strategies for the protection of this unique and vulnerable flora. Full article

Blueberry breeding requires a significant commitment of time, skilled labour, and financial resources, but it is essential to develop new cultivars that can meet challenges such as climate change, disease resistance, and changing market preferences. Intraspecific hybridisationis a widely used breeding strategy to increase genetic diversity, broaden the selection base, and develop new cultivars. By crossing different varieties and making advanced selections, breeders can introduce desirable traits such as improved fruit quality, increased yield, improved disease resistance and greater adaptability to environmental conditions. The present study aimed to evaluate the heritability of some key biometric and biochemical parameters inblueberry hybrids derived from intraspecific crosses to assess their inheritance patterns. The results can guide breeders in selecting parent combinations that maximise genetic gain, ultimately supporting the advancement of commercial blueberry production. The ‘Delicia × 4/6’ hybrid combination showed excellent performance for total polyphenol content, flavonoids, tannins, and ascorbic acid, with high genetic gain and near complete heritability, making it a promising candidate for improving antioxidant activity. The ‘Azur × Northblue’ hybrid had favourable total anthocyanin and tannin content, but an unfavourable sugar and ascorbic acid profile.The ‘Simultan × Duke’ hybrid combination showed the highest genetic gain for total soluble solids and firmness, together with high positive heterosis and heterotic progress, highlighting its potential for high-quality cultivars suitable for mechanical harvesting and storage. This research provides valuable insights into the efficiency of intraspecific hybridisationin the development of new blueberry cultivars with improved agronomic and nutritional qualities. Full article

As urban populations continue to rise, cities face mounting challenges related to infrastructure strain, resource management, and environmental degradation. Sustainable urban development has emerged as a crucial strategy to balance economic growth, social equity, and environmental preservation. In this context, artificial intelligence offers transformative potential, particularly through predictive modeling, which enables data-driven decision making for more efficient and resilient urban planning. This paper explores the role of AI-powered predictive models in supporting sustainable urban development, focusing on key applications such as infrastructure optimization, energy management, environmental monitoring, and climate adaptation. The study reviews current practices and real-world examples, highlighting the benefits of predictive analytics in anticipating urban needs and mitigating future risks. It also discusses significant challenges, including data limitations, algorithmic bias, ethical concerns, and governance issues. The discussion emphasizes the importance of transparent, inclusive, and accountable AI frameworks to ensure equitable outcomes. In addition, the paper presents comparative insights from global smart city initiatives, illustrating how AI and IoT-based strategies are being applied in diverse urban contexts. By examining both the opportunities and limitations of AI in this domain, the paper offers insights into how cities can responsibly harness AI to advance sustainability goals. The findings underscore the need for interdisciplinary collaboration, ethical safeguards, and policy support to unlock AI’s full potential in shaping sustainable, smart cities. Full article

Forests in the Tolna region (Hungary) are distributed at the xeric limit of broadleaved forest zones and adapted to the arid ecological conditions of the wood-steppe climate. An 85-year-old in situ gene conservation stand of Quercus virgiliana mixed with other taxa of section Quercus was studied, which was regenerated naturally by both seedlings and coppicing. To analyze the phenotypes growing within the stand and the genetic structure of the population, a total of 138 trees were sampled. For taxonomic classification, a complex of morphological traits of oak taxa growing naturally in the region was used. Out of the 12 morphotype groups, only a few trees were classified as Q. virgiliana (eight individuals) or Q. robur (nine individuals), and the majority of the trees (121 individuals) were hybrid or introgressed phenotypes of Q. virgiliana adapted to xeric conditions by its xeromorphic traits. Despite the high number of coppiced trees (89 pcs vegetatively regenerated), the genetic variation was relatively high based on 16 nSSR markers used for analyses. Some of the trees were classified as non-autochthonous with Slavonian oak origin, both by morphological traits and SSR structure. Despite some alleles being lost, the allelic diversity of the seedling trees’ group was similar to that of the group of parent generation (coppiced trees). The spatial structure of trees supported the results of morphologic classification, and Q. virgiliana and hybrid phenotypes were growing on xeric microhabitats of the stand, mostly on southeast-facing slopes or ridges of hills. Consequently, the stand might fulfill all the in situ gene conservation requirements based on the high genetic diversity measured and the high number of xeromorphic phenotypes in the context of climate change as well. Full article