This study presents a systematic bibliometric framework to evaluate global morphometric research on Apis mellifera published between 1982 and 2024. Using a PRISMA-guided screening process and bibliometric analysis conducted with the Bibliometrix R package, a total of 199 peer-reviewed studies were analyzed to identify publication trends, methodological shifts, thematic structures, and geographic patterns. Bibliometric indicators highlight significant regional research disparities, with contributions concentrated in South America, Africa, and parts of Europe, while certain subspecies and hybrid zones remain underrepresented. The thematic evolution analysis reveals a growing convergence of morphometric methods with molecular genetics, population structure analysis, and environmental monitoring, emphasizing the increasing interdisciplinary nature of this field. This study contributes to the literature by offering a comprehensive, reproducible overview of the intellectual evolution of honey bee morphometrics, with a specific focus on its application to conservation strategies, subspecies identification, and the monitoring of environmental changes. The proposed framework serves as a valuable reference for future research that integrates morphometric techniques into global efforts addressing agricultural sustainability, biodiversity conservation, and climate change.
Abstract In a pot experiment, the dry seeds of the Sweet Marjoram plant ( Origanum majorana L.) were exposed to ultraviolet UV-C (254 nm) light for 30 and 60 min. They were subjected to salinity conditions (4 and 6 dS/m). Plants were evaluated, determining total chlorophyll (SPAD), oil content, and active components in the vegetative phenotypes. The results showed that plants grown under UV for 60 min increased plant height by 67.5% and fresh weight (FW) by 45.5% compared to the corresponding control under non-saline conditions. Similarly, exposing seeds to UV-C for 60 min increased the total chlorophyll group by 52% at 6 dS/m. They were less affected by salt stress, while treating them for 30 min improved the number of branches and leaves under non-saline conditions. Additionally, it helped plants protect themselves by shielding them from osmotic shock. Also, Ultraviolet rays led to a reduction in lipid peroxidation malondialdehyde (MDA) levels and increased the total phenols concentration under UV for 60 min, despite their growth being better than that of the control group. However, treating seeds with UV light for 60 min enhanced the biosynthesis of essential oils in plants. Also, GC–MS analysis detected a total of 30 compounds. Nevertheless, the major components were trans-Sabinene hydrate, terpinen-4-ol, γ-terpinene, α-terpineol, terpinolene, and β-myrcene. In general, it can be concluded that treating marjoram seeds with UV-C light stimulated the plant's ability to tolerate salt stress by activating the antioxidant system.
Vegetable oil production has expanded rapidly over the past two decades, raising concerns about its environmental consequences. This study examines the impact of palm oil, soybean oil, sunflower oil, and rapeseed oil cultivation on agriculture-driven deforestation across six continents over 23 years (2001–2023). Using secondary data from the Food and Agriculture Organisation and Global Forest Watch, this study has constructed a balanced panel dataset of 138 observations (6 continents × 23 years). Panel regression analysis was employed, with model selection guided by Hausman and Breusch-Pagan tests, ultimately favouring the Random Effects estimator. Results indicate that palm oil (β = 0.109, p < 0.05) and soybean oil (β = 0.389, p < 0.01) are strongly associated with forest loss, particularly in Asia and South America. Sunflower oil also shows a positive impact (β = 0.189, p < 0.05), while rapeseed oil demonstrates a negative impact (β = − 0.151, p < 0.05), suggesting comparatively lower deforestation pressures in temperate regions. Confidence intervals and standard errors confirm the robustness of these findings. By integrating multiple oil crops simultaneously, this study provides a comprehensive assessment of how global demand for vegetable oils translates into forest loss. The results highlight the need for crop-specific and region-specific strategies to mitigate environmental impacts while balancing agricultural development with forest conservation.
Abstract Ginger is an herbaceous perennial plant and recognized as one of the most economically valuable plants globally. This study provides the first report on foliar amino acid application, both alone and synergistically with micronutrients, for ginger production within a substrate culture system designed to provide a more sustainable alternative to traditional forest-clearing methods. The objectives of this study were: (1) to evaluate the interactive effects of combining amino acid and micronutrient foliar applications on the overall growth, morphological traits, and rhizome quality of ginger cultivated in a substrate culture system; and (2) to establish an optimized nutritional strategy using foliar feeding that maximizes ginger yield and quality while supporting sustainable agricultural practices. The experiment was arranged as a 2 × 4 factorial experiment in a completely randomized design with 5 replications. The first factor was a ginger variety (Nakhon Si Thammarat: NST and Trang: TRG varieties), and the second factor was a foliar application (control, amino acid, micronutrient, and amino acid+micronutrient). The results revealed that while the TRG variety showed higher yield potential (up to 1.55 kg total rhizome weight), the NST variety was more responsive to quality enhancement, achieving a maximum individual rhizome weight of 205.07 g under micronutrient (M) application and a total phenolic content of 45.85% and fat content of 6.79% under the A + M treatment. The M treatment significantly boosted protein levels to 12.16% in NST. The findings underscore that combined foliar applications produce synergistic effects across multiple agronomic traits, specifically maximizing total phenolic content and fat content in the NST variety, supported by strong positive correlations between root development and final rhizome weight ( r ≈ 0.81–0.98). This approach offers practical guidance for improving ginger cultivation in controlled environments. Further investigation should include multi-location field trials with various genotypes and long-term assessments of sustainability, cost-benefit, and environmental impacts.
Abstract Date palm ( Phoenix dactylifera L.) productivity is increasingly threatened by inflorescence rot caused by Mauginiella scaettae . This study assessed the response of ten Moroccan cultivars using a controlled spikelet inoculation assay, biochemical profiling, and evaluation of the effects of spikelet extracts on fungal growth. Susceptible cultivars ( Mejhoul , Boufgous and Bousthami ) showed rapid symptom development and stimulated fungal growth in vitro, whereas tolerant cultivars ( Tadment , Najda and Sair-Lyalat ) displayed delayed symptom expression and strong antifungal activity. More tolerant cultivars accumulated higher levels of phenolics, flavonoids, condensed tannins, and antioxidant activity evaluated by three complementary antioxidant assays (DPPH, ABTS, and FRAP), whereas susceptible cultivars contained lower levels of these defensive metabolites and may have contained higher amounts of readily available nutritive compounds that favored fungal proliferation. To our knowledge, this is the first study to develop and apply a controlled spikelet-based assay to assess date palm cultivar response to M. scaettae under experimental conditions. These findings indicate that the response to M. scaettae depends on the balance between antifungal secondary metabolites and pathogen-promoting nutrients, thereby providing useful insights for breeding and disease management.
Abstract A two-year field study was conducted to study the possibility of partially replacing the nitrogenous fertilizer with the inoculum in a sandy land without affecting the growth, yield, and quality characteristics of sweet potato-sweet corn intercropping systems. The first factor was nitrogen fertilizer: Azospirillum brasilense (Ab, T1), 100 kg N ha − 1 (half-dose of nitrogen, T2), 200 kg N ha − 1 (full dose of nitrogen, T3), Ab + 100 kg N ha − 1 (T4), Ab + 200 kg ha − 1 (T5). The second factor was the intercropping systems (IS): (1) 100%IS =sole sweet corn or sole sweet potato, (2) 75%IS = 3 rows of sweet corn + one row of sweet potato, (3) 50%IS = two rows of each crop, (4) 25%IS = one row of sweet corn + 3 rows of sweet potato. The interaction showed that all intercropping systems within T5 surpassed other treatment combinations in all the studied traits, but it was comparable to the intercropping systems within T4. The lowest results were observed in all traits within the bacteria treatment (T1). The highest significant fat percentage and total solids were found in T5 + IS in sweet potato. Land equivalent ratio was greater than one in all treatment combinations, but the highest values were observed in T5 + 50%IS and T1 + 50%IS. As inoculating intercropped plants with Azospirillum spp. with either full- or half-dose of nitrogenous fertilizer under sandy land conditions had comparable growth and yield for both crops, we conclude that integrating intercropping (50%IS) with microbial inoculation and a reduced (half) nitrogen fertilizer dose can effectively lessen the reliance on synthetic nitrogen inputs. The best return was achieved by intercropping sweet potato with sweet corn at a ratio of 50% for each, combined with Azospirillum spp. + 200 N ha − 1 .
Abstract This study evaluated the effects of selenium (Se) application rates (0, 25, 50, and 75 mg/m²) and arbuscular mycorrhizal fungi (AMF) inoculation on growth, forage yield, physiological responses, and tissue Se accumulation of Cichorium intybus cv. Cropmate under controlled water limit conditions. A 4 × 2 factorial pot experiment was conducted with drought stress imposed from 14 days after sowing. Morphological traits were recorded weekly, while forage yield, root traits, chlorophyll content, relative water content, proline, total phenolics, flavonoids, and tissue Se content were determined at harvest. Both Se and AMF significantly affected leaf number, plant height, and stem diameter (P < 0.05), although leaf width remained unaffected. At week VI, the 75 mg Se + AMF maximized height plants, exceeding the control by ~ 63.5%, whereas the 50 mg Se + AMF yielded the highest leaf number (14.40) and stem diameter (15.20 mm). The same combination resulted in the greates fresh forage and crude protein yields, with increases of ~ 51.9% and ~ 170%, respectively, relative to the control (P < 0.05). Selenium application and AMF inoculation together enhanced chlorophyll and relative water content, while lowering proline, phenolic, and flavonoid levels, indicating improved drought tolerance. Tissue Se content increased with increasing Se dose, reaching 0.65 µg/g at the highest rate, while AMF appeared to modulate Se translocation. We conclude that a moderate Se dose (50 mg/m²) combined with AMF offers a promising strategy for chicory biofortification under drought conditions. Field validation and Se speciation analyses are warranted before farm-scale adoption can be recommended.
Abstract Self-supervised learning (SSL) has emerged as a pivotal paradigm in computer vision, particularly for applications where labeled data are limited, costly, or labor-intensive to obtain. This paradigm is especially relevant to sustainable agricultural systems, where large-scale manual annotation is impractical, and resource efficiency is a key objective. In agricultural vision tasks such as potato disease detection, quality assessment, and automated inspection, SSL has demonstrated strong potential to address the substantial visual variability encountered under real-world field conditions. Among recent SSL approaches, the Distillation with No Labels (DINO) framework, built upon Vision Transformers (ViTs) and multi-crop data augmentation, has shown remarkable capability in learning robust and transferable feature representations. In this study, we present a comprehensive analytical review of the DINO framework within the context of sustainable automated potato inspection systems in the Al Kharj region. The analysis systematically investigates architectural design choices, critical hyperparameters, teacher–student distillation dynamics, optimization behavior, and the mathematical mechanisms employed to prevent representation collapse. Using a potato leaf disease dataset, the DINO-ViT model is evaluated under rigorous experimental conditions. The results demonstrate that DINO-ViT effectively learns compact and semantically meaningful feature representations with clear inter-class separability, as evidenced by principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE) visualizations, and quantitative class-separation metrics. Furthermore, when compared with conventional machine learning approaches, including Random Forest, Support Vector Machines, Gradient Boosting, and k-Nearest Neighbors, the DINO-ViT model achieves superior performance, attaining an accuracy of 0.8344 and an F1-score of 0.8334.
Abstract Comparative data on sediment biogeochemistry in vegetated and non-vegetated coastal habitats remain limited in arid regions. This study aims to compare sediment physicochemical properties, carbon and nitrogen content, grain-size fractions, transition metals, and rare earth elements in mangrove and adjacent mudflat sediments along the eastern coast of Saudi Arabia. Significant spatial variability was observed across all sediment properties and elemental groups, with location exerting a stronger influence than habitat type or sediment depth. Mangrove sediments showed site-specific differences in carbon content, carbonate concentration, and C:N ratios, whereas mudflat sediments were comparatively uniform. Transition metals and REEs were consistently enriched at Syhat across both habitats, while Tarut exhibited the lowest concentrations and Darin showed intermediate values. Habitat-related contrasts were element-specific, with some metals showing higher mean concentrations in mangroves and others in mudflats. Multivariate analyses revealed stronger associations between carbon and multiple metals and REEs in mangrove sediments, whereas carbon–element relationships were weaker and more dispersed in mudflats, indicating contrasting geochemical controls. Overall, the results demonstrate that site-specific factors dominate sediment geochemistry in arid mangrove–mudflat systems, highlighting the importance of spatial context when evaluating elemental dynamics and coastal ecosystem functioning.
Abstract Technology adoption extends beyond mere usage; it encompasses the provision of essential resources and equipment, as well as the recommendation of performance experiences. Institutions must actively address these factors to facilitate effective individual adoption. This study aimed to explore rice farmers' perceptions of personal protective equipment (PPE) characteristics and their influence on the intentions to prepare, use, and recommend PPE to fellow farmers. A survey involving 307 rice farmers in Golestan Province, northwestern Iran, was conducted to collect data. Exploratory factor analysis identified three distinct characteristics of PPE: PPE effectiveness, PPE uncomfortableness and PPE unattractiveness. Binary logistic regression analyses revealed that PPE effectiveness positively influences all three intention categories: the intention to prepare, the intention to use, and the intention to recommend PPE. In contrast, both PPE uncomfortableness and PPE unattractiveness negatively impact these intentions. Additionally, factors related to farmers' financial capacity—such as annual income, land size, and access to on-site storage—positively influence the intention to prepare PPE. Variables associated with agricultural extension and training, including interactions with extension agents and reliance on expert advice, also positively affect the intention to use PPE. Furthermore, active participation in local organizations significantly enhances the intention to recommend PPE to peers. To promote safer farming practices, agricultural policy interventions must increase awareness of PPE effectiveness. Additionally, creating financial support mechanisms to assist farmers in acquiring and developing PPE should be prioritized by policymakers.
Abstract Agricultural exports play a crucial role in the Jordanian agricultural sector and understanding their market dynamics is very important for sustaining their competitiveness. This study evaluates the export competitiveness of the Jordanian tomatoes using a near ideal demand system (AIDS) model and a regional survey of exporters. The research estimates the market demand, identifies major importing countries of the Jordanian tomatoes and examines price responsiveness in key markets such as Kuwait and the UAE. Results indicate that the Jordanian tomato demand is price-sensitive, with higher export prices associated with reductions in demand, though the magnitude of these effects varies across markets. Cross-price elasticity identifies Syria and Lebanon as primary competitors and other regional competitors like Turkey, India and Oman as well. While the AIDS model provides strong econometric insight, the study is limited by the small sample size (n = 20) of the qualitative survey. The research fills the gap in understanding the demand dynamics for Jordan's key agricultural exports, offering strategic pricing and infrastructure recommendations for policy makers. Originality/value: This is one of the few studies that combines demand system modeling with direct exporter response in the Jordanian context. To support the export growth we suggest that policy measures such as pricing, improved market access and enhanced quality standards may be beneficial. Market analysis is recommended to understand the consumers preferences, also increasing investment in transportation and refrigerated storage infrastructure would enhance the Jordanian tomato competitiveness and export markets position.
Abstract This study examines the dynamic demand behavior for poultry, red meat, and fish in Saudi Arabia over the period 1990–2024, employing the dynamic Almost Ideal Demand System (ECM–AIDS) framework. The analysis relies on official annual data to estimate both short-run and long-run expenditure share equations, as well as own-price, cross-price, and expenditure elasticities for the three meat groups. The empirical results demonstrate strong explanatory power in the short run, with adjusted R² values of 0.888 for poultry and 0.865 for red meat, while the relatively lower value for fish reflects its modest share in total meat expenditure. The error correction terms are negative and statistically significant across all equations, confirming the existence of a long-run equilibrium relationship and indicating an annual adjustment speed of approximately 28%. In the long run, the model exhibits a high degree of goodness of fit, with adjusted R² values exceeding 0.90. Short-run own-price elasticities indicate that demand for poultry and red meat is price inelastic, while fish demand is also inelastic but relatively less responsive. In the long run, price elasticities increase moderately, reflecting greater consumer adjustment over time. Cross-price elasticities confirm substitution relationships among the different meat types. Expenditure elasticities reveal that poultry demand is more responsive to income growth, while red meat and fish exhibit relatively lower income responsiveness. Overall, the findings indicate a gradual structural shift in food consumption patterns in Saudi Arabia toward poultry, reflecting evolving dietary preferences and broader socioeconomic changes.
Abstract Palm tree cultivation in the Al-Kharj region is significantly affected by a range of diseases that compromise both yield and crop quality. We propose a robust framework for palm tree disease classification that leverages transfer learning in combination with a Chaotic Red Panda Optimization (CRPO) algorithm. The framework effectively tackles challenges posed by small and imbalanced datasets through automated dataset exploration, class discovery, and visualization of sample distributions. The CRPO algorithm utilizes a chaotic logistic map to generate candidate fully connected layer weights, thereby optimizing the network before fine-tuning with the Adam optimization algorithm. Comprehensive visualizations illustrate the dynamics of the optimization process, including fitness evolution, population diversity, and chaotic sequences, as well as training progression and evaluation metrics. Experimental evaluation on a dataset of 3,089 images spanning nine disease categories and healthy palms demonstrates robust classification performance, with precision, recall, and F1-scores ranging from 0.98 to 1.00 across all classes.
Abstract Pomegranate is a fruit of substantial economic and nutritional importance worldwide. However, its production and quality are significantly threatened by a range of diseases, whose early and accurate detection remains a challenging task. Traditional disease identification relies primarily on manual inspection by agronomists, a process that is time-consuming, labor-intensive, and often leads to delayed intervention, resulting in considerable crop losses. To address these limitations, this study proposes a dual-model deep learning framework that integrates advanced deep learning techniques with iterative refinement for both disease classification and detection, leveraging the YOLOv11 architecture. A key contribution of this work is the development of a comprehensive Pomegranate Fruit Diseases Dataset, consisting of 5,099 carefully annotated images spanning five classes: Alternaria fruit spot, Anthracnose, Bacterial blight, Cercospora fruit spot, and healthy fruits. In the classification module, a deep learning–based model enhanced through iterative refinement is employed to achieve robust disease recognition. The model is extensively optimized using advanced hyperparameter tuning and progressive learning strategies. Experimental results demonstrate strong classification performance, achieving an F1-score of 0.988 for Cercospora fruit spot and 0.950 for Anthracnose. In the detection module, YOLOv11 is adopted to accurately localize and identify disease regions within pomegranate images. Model performance is further improved through systematic data augmentation, anchor box optimization, and fine-tuned hyperparameters. Quantitative evaluation confirms the effectiveness of the proposed detection framework, yielding a precision of 0.9253, recall of 0.8558, AP@50 of 0.9453, mAP@50–95 of 0.8456, and an overall fitness score of 0.8556. These results highlight the capability of the proposed YOLOv11-based system to accurately detect and classify both common and complex pomegranate disease patterns, offering a reliable and efficient solution for precision agriculture and early disease management.
Abstract Nitrogen (N) is essential nutrient for plant growth and development, and this nutrient is mostly supplied to the plant through organic and inorganic fertilizer. However, approximately 50% of the nitrogen applied to crops is actually absorbed by them, with the other half lost to the environment through processes like leaching, volatilization, runoff, and denitrification. This inefficiency not only wastes resources but also contributes to environmental pollution and greenhouse gas emissions. To mitigate these issues, improving nitrogen use efficiency (NUE) is essential for better fertilizer management and enhanced crop production. Improving NUE by targeted agronomic techniques and high-throughput technology might reduce the dependence on excessive N inputs and mitigate related environmental consequences. The integration of agronomic management with genetic and biotechnology techniques improves N absorption and assimilation in crops. This integrated strategy may result in more sustainable agricultural systems, addresses global food requirements, and conserves natural resources. This review study consolidates the existing research on nitrogen losses, identifies critical factors which can influence NUE, and explores both agronomic and genetic strategies.
Abstract Monocropping systems of Zea mays L. in Brazilian biomes exert strong pressures on the functionality of Cambisols. This study aimed to assess the influence of organic fertilization on the chemical properties of Cambisols across four Brazilian biomes (Amazon, Atlantic Forest, Caatinga, and Cerrado), as well as on plant nutrient extraction patterns, the microbial community within the rhizosphere of Z. mays, and crop yield. Under greenhouse conditions over three cultivation cycles, soil pH, soil organic carbon (SOC), available P, total N, plant dry biomass, yield, macro- and micronutrient extraction, microbial gene abundance, and dsDNA content were evaluated in Cambisols from different Brazilian biomes under two treatments (organic fertilization × control). Higher values of SOC, available P, total N, shoot and root dry biomass, yield, extraction of N, P, Mg, S, B, Cu, Fe, and Zn, as well as bacterial and fungal gene abundance and dsDNA content, were observed in soils receiving organic fertilization. The findings suggest that organic fertilization enhances plant performance, soil fertility, nutrient dynamics, and rhizosphere functioning in Cambisols, contributing to improved maize yield. Organic inputs were associated with (i) increases in SOC, available P, and total N; (ii) greater abundance of bacterial and fungal genes and higher dsDNA content; (iii) enhanced extraction of macro- and micronutrients; and (iv) the potential to support the conservation of Cambisols while contributing to food security and sustainable agricultural practices.
Abstract Habitat loss and land-use changes in tropical forests have significantly altered the structure and composition of natural ecosystems, contributing to a decline in biodiversity. By integrating diverse approaches that assess both species diversity and functional traits, a more comprehensive understanding of the ecological impacts of habitat transformation can be achieved. In this study, we aimed to assess how habitat structure and resource seasonality influence the abundance, richness, and composition of fruit-feeding butterflies across different forest amounts and productivity periods in Madagascar’s dry forests. We collected data on fruit-feeding butterflies and habitat variables (including density of tree-shrub, herbaceous cover, litter, and Enhanced vegetation index (EVI)) across nine 6 × 6 km landscapes with varying levels of forest cover. The data were grouped over three productivity periods (low, medium, and high) between 2023 and 2024. We recorded 5,997 individuals of 16 species across nine landscapes with varying levels of forest amount. Species occurrence was positively correlated with high productivity periods and extensive forest amounts, while species richness was higher during medium productivity. The variation in the space–time availability of resources has the most important variables: Enhanced vegetation index (EVI) and density of trees and shrubs. So, the communities have their dynamics related to productivity and habitat structure at different scales, vegetation and ecological processes are evidenced mainly in landscapes of low and high forest cover, strongly associated with the seasonality of the reserve. The species of butterflies to changes in the environment in different ways, being possible to observe the most accentuated effects in specific characteristics, where the productivity and structural complexity of microhabitats allow the alternation of species/functional groups throughout the year and landscapes.
Abstract A laboratory and warehouse study was conducted during the 2024/2025 season in the laboratories and warehouses of the Plant Protection Department of the College of Agriculture, Tikrit University/Iraq. The study included the use of silver nanoparticles and biosynthetic zinc nanoparticles from the food mushroom Pleurotus ostreatus A2019 for four parts: (mushroom filtrate, biomass, cold extract and Hot extract) to preserve the safety and health of seeds from black point disease for six varieties of Iraqi wheat. The results of the study showed that the two fungi, Altrnaria spp and Fusarium spp, had the highest appearance rate among the isolated and diagnosed fungi, as the percentage of the two fungi reached 17.50% and 19.25%, respectively. The experimental treatment was reduced to study the effect Concentrations of silver nanoparticles and zinc nanoparticles on the fungi Alternaria spp and Fusarium spp, which cause black spot disease (cm). Treating the fungus filtrate with silver nanoparticles (AgNPs) at a concentration of 2 mm gave the highest rate of inhibition, reaching 0·640 cm for the fungus Alternaria spp and 0·520 cm for the fungus Fusarium spp at the same concentration and the same treatment, while the highest percentage was Germination before storage for the Iraq 2 variety reached 92.25%, while the highest germination rate after storage was for the Aba variety, which reached 97.75%. As for estimating the storage infection rate based on the pathogenic fungus Fusarium spp, the lowest infection rate was in the treatment consisting of (AgNPs) + pesticide + fungus, as it reached 0.79% and 0.75% for the fungus Alternaria spp for the same treatment and the same concentration.
Abstract The growing demand for sustainable fuel alternatives highlights the need to optimize agricultural waste briquettes, as their current performance in terms of energy output and durability remains limited for consistent daily use. In this study, briquettes were produced from carbonized agricultural wastes (cocopeat, corn cobs and bean shell) using starch and clay as binder. Central Composite Design (CCD) of Response Surface Methodology (RSM) was used to investigate the effect of binder type, biochar type, binder percentage and amount of water on calorific value, compressive strength, ash content and shatter index. Carbonization was performed by conventional heating at temperatures ranging from 300 – 500 °C and residence times of 45 min – 120 min. RSM analysis showed that the experimental results were most accurately described by quadratic models. The parameters were optimized at 91.08 mL of water, 34.14% starch binder, and corn cob biochar, resulting in a calorific value of 17.86 MJ/kg, compressive strength of 21.61 N/mm2, shatter index of 99.82%, and ash content of 5.59%. This study supports the development of alternative fuels for domestic use in developing and underdeveloped countries.
Abstract Postharvest fungal decay threatens fruit and vegetable quality and safety, and the movement of produce across borders facilitates the spread of cosmopolitan pathogens. Comprehensive molecular data from such cross-border supply chains remain limited. A total of 78 symptomatic and asymptomatic samples of tomato, capsicum, grape, strawberry, zucchini, lemon, apple, pear, and banana were collected from an international produce market. Fungi were isolated on PDA (Rose Bengal dye) and characterized through sequencing of the ITS rDNA region, followed by phylogenetic placement against type and epitype sequences. Pathogenicity was assessed on corresponding healthy hosts using wounded and non-wounded inoculations. Lesion diameters were analyzed non-parametrically: Kruskal–Wallis (10,000-permutation p) with Dunn–Benjamini–Hochberg, and exact Wilcoxon with Cliff’s δ (95% CI) for wounded vs non-wounded within isolates. Twenty-seven isolates were resolved into 19 species across nine genera. Penicillium (9 isolates), Aspergillus (5), Fusarium (4), and Talaromyces (4) dominated. Some taxa infected intact tissues, including Mucor irregularis on zucchini and Fusarium inflexum on banana. Others, such as Penicillium expansum (apple), Talaromyces aculeatus and Fusarium falciforme (lemon), required wounds. On tomato, Penicillium oxalicum produced the largest lesions regardless of wounding. Significant effects were host‑ and isolate‑specific; a wounding effect was detected for grape (GL88). Fruits and vegetables act as vehicles for fungal pathogens that cross borders and establish themselves in new environments. Molecular characterization revealed a diverse set of cosmopolitan taxa with distinct host and wound dependencies. Effective biosecurity measures and careful handling are critical to limit the transboundary risks of postharvest fungal decay.
