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Background Mung bean ( Vigna radiata ) is an important, underutilized legume known for its nutritional value and ecological adaptability, making it a potential crop for combating global food insecurity, particularly under climate change stress. This study aimed to assess the genetic diversity of 75 mung bean accessions from seven countries using Start Codon Targeted (SCoT) markers. Results A total of 24 primers were evaluated, and 15 were selected for further analysis based on polymorphism. The primers produced 304 bands, 264 of which were polymorphic, indicating high genetic diversity among the mung bean accessions. The average polymorphism rate was 88.22%, and the mean polymorphism information content (PIC) value was 0.23, suggesting moderate marker informativeness. Genetic diversity indices, including Nei’s gene diversity (h), the effective number of alleles (Ne), and Shannon’s information index (I), ranged from 0.27 to 0.42, demonstrating moderate variability in the studied population. The genetic relationship analysis using Neighbor-Joining (NJ) trees and Principal Coordinate Analysis (PCoA) divided the accessions into two main groups, with further subgroups identified, reflecting the complex genetic structure within the germplasm. The study also identified significant intra-population variation (87%), indicating substantial genetic admixture. Conclusions These results provide insights into the genetic resources of mung bean, supporting the development of breeding programs aimed at enhancing climate resilience, yield stability, and nutritional quality. The findings underscore the importance of conserving and utilizing genetic diversity in mung beans to ensure food security in the face of environmental challenges.

Biotechnology has revolutionized the agricultural landscape, ushering in a new era of crop improvement. Biotechnological delivery innovations have driven significant advancements, from enhancing nutritional value and shelf life to developing stress-resistant varieties. Leveraging techniques like genome editing, RNA interference (RNAi), and omics approaches, the potential to generate tolerant crops, create beneficial germplasm, achieve higher crop yields, and enable targeted biomolecule delivery has been unlocked, leading to the establishment of novel, sustainable agricultural systems. This review synthesized 481 studies sourced from the Web of Science (WOS) database, reflecting the diversity of plant species, biotechnological approaches, and abiotic/biotic stress categories reported in the literature over the past three years. The findings focused on specific applications and implications of various technologies across different stress categories and plant types, providing a detailed perspective on stress tolerance mechanisms. Furthermore, the review highlights significant areas of controversy, including ethical concerns, debates, challenges, risks, socioeconomic impacts, and limitations associated with these technological advancements. As the world’s population surges and dietary demands evolve, biotechnology holds the key to assuring secure food supplies and promoting sustainable agricultural practices amidst the challenges brought about by climate change. This synthesis highlights the significant potential of biotechnological advancements in revolutionizing agriculture, facilitating the creation of resilient, adaptable, and sound systems capable of addressing the needs of a swiftly evolving world.

As global climates shift, plants are increasingly exposed to biotic and abiotic stresses that adversely affect their growth and development, ultimately reducing agricultural productivity. To counter these stresses, plants produce secondary metabolites (SMs), which are critical biochemical and essential compounds that serve as primary defense mechanisms. These diverse compounds, such as alkaloids, flavonoids, phenolic compounds, and nitrogen/sulfur-containing compounds, act as natural protectants against herbivores, pathogens, and oxidative stress. Despite the well-documented protective roles of SMs, the precise mechanisms by which environmental factors modulate their accumulation under different stress conditions are not fully understood. This review provides comprehensive insights into the recent advances in understanding the functions of SMs in plant defense against abiotic and biotic stresses, emphasizing their regulatory networks and biosynthetic pathways. Furthermore, we explored the unique contributions of individual SM classes to stress responses while integrating the findings across the entire spectrum of SM diversity, providing a comprehensive understanding of their roles in plant resilience under multiple stress conditions. Finally, we highlight the emerging strategies for harnessing SMs to improve crop resilience through genetic engineering and present novel solutions to enhance agricultural sustainability in a changing climate.

Background Genotypic differences in nitrogen use efficiency strongly influence sorghum growth and yield, highlighting the need for precise and reliable prediction of cultivar responses to nitrogen (N) availability. This study investigates the impact of two N treatments on sorghum cultivars, using artificial intelligence (AI) models for prediction. Results A randomized complete block design with two treatments: 0 kg N ha − 1 (0 N) and 238 kg N ha − 1 (238 N) was used. Six hybrid sorghum cultivars (Gustav, Estyphon, Foehn, Vegga, Aday1 and Beydarı) were evaluated for different traits. Statistical analysis included two-way ANOVA and factorial regression to assess treatment effects. Significant treatment effects were observed. Beydarı and Estyphon exhibited larger stem diameter and leaf area under 238 N, while Aday1 had the smallest values under 0 N. Gustav showed the highest panicle width, panicle weight, and grain yield under 238 N. Stomatal conductance showed an opposite trend, decreasing with N supplementation. Machine learning models, specifically Random Forest (RF) and Light Gradient-Boosting Machine (LightGBM), were used to model the interaction, achieving R 2 values ranging from 0.759 to 0.966 for RF and 0.729 to 0.980 for LightGBM, indicating strong predictive accuracy. Conclusion LightGBM consistently achieved R 2 values greater than 0.92 for key traits, such as stomatal conductance, panicle width, and grain yield, demonstrating its potential to optimize N management. Gustav performed best under high N, whereas cultivar responses to low N were genotype-specific, captured effectively by the machine learning models. These findings highlight the role of AI models in predicting cultivar performance and supporting sustainable agricultural decisions.

The world is facing rapid climate change and a fast-growing global population. It is believed that the world population will be 9.7 billion in 2050. However, recent agriculture production is not enough to feed the current population of 7.9 billion people, which is causing a huge hunger problem. Therefore, feeding the 9.7 billion population in 2050 will be a huge target. Climate change is becoming a huge threat to global agricultural production, and it is expected to become the worst threat to it in the upcoming years. Keeping this in view, it is very important to breed climate-resilient plants. Legumes are considered an important pillar of the agriculture production system and a great source of high-quality protein, minerals, and vitamins. During the last two decades, advancements in OMICs technology revolutionized plant breeding and emerged as a crop-saving tool in wake of the climate change. Various OMICs approaches like Next-Generation sequencing (NGS), Transcriptomics, Proteomics, and Metabolomics have been used in legumes under abiotic stresses. The scientific community successfully utilized these platforms and investigated the Quantitative Trait Loci (QTL), linked markers through genome-wide association studies, and developed KASP markers that can be helpful for the marker-assisted breeding of legumes. Gene-editing techniques have been successfully proven for soybean, cowpea, chickpea, and model legumes such as Medicago truncatula and Lotus japonicus . A number of efforts have been made to perform gene editing in legumes. Moreover, the scientific community did a great job of identifying various genes involved in the metabolic pathways and utilizing the resulted information in the development of climate-resilient legume cultivars at a rapid pace. Keeping in view, this review highlights the contribution of OMICs approaches to abiotic stresses in legumes. We envisage that the presented information will be helpful for the scientific community to develop climate-resilient legume cultivars.

Maize ( Zea mays L.) is a cornerstone of global agriculture, contributing significantly to food security and economic stability as one of the world’s most important cereal crops. Breeding programs enhancing maize productivity depend on strategically exploiting genetic variation and heterosis. This study employed biometrical approaches to analyze combining ability, heterosis, and heritability in 29 genotypes, including 9 parental lines and 20 F1 hybrids, developed through a Line × Tester mating scheme. Significant genetic variability was observed, with P4 (B73) and P1 (Zheng58) identified as superior combiners for nitrate reductase (NR) activity, glutamine synthetase (GS) activity, and grain yield. Testers P8 (Mo17) and P9 (PH4CV) exhibited strong combining abilities for NR activity, ear length, and grain yield, indicating the importance of parental selection. Additionally, hybrids P1 × P9 (Zheng58 × PH4CV) and P5 × P7 (PH6WC × 178) exhibited strong specific combining ability (SCA) effects, signifying both additive and non–additive gene actions in trait improvement. High mid–parent heterosis (MPH) and better–parent heterosis (BPH) were observed, with MPH ranging from 61.91% to 272.26% and BPH from 32.77% to 216.29% for grain yield, showing the potential for hybrid vigor. High heritability for grain yield, NR activity, and other traits suggests a strong genetic foundation for breeding. These findings highlight the integration of genetic variability, combining ability, and heterosis, optimizing hybrid performance and enhancing parental selection in future breeding programs.

Perennial ryegrass ( Lolium perenne L.) is a key perennial species with significant agricultural and ecological importance. Salt stress adversely affects plant growth by inducing oxidative stress, reducing biomass accumulation, and impairing physiological functions. In this study, calcium chloride, magnesium chloride, magnesium sulfate, and sodium sulfate treatments were applied to evaluate their effects on salinity-induced molecular and physiological responses. The effects of these treatments on the expression of salt stress–responsive genes Ascobate Peroxidase (APX), Glutathione Reductase (GR), Heavy Metal ATPase (HMA), and Phytochelatin Synthase (PCS) were analyzed using quantitative real-time PCR (qRT-PCR). In addition, agronomic traits including seedling length, fresh and dry weight, plant water content, and dry matter ratio were evaluated. Higher salinity increased stress-related gene expression, but this was not enough to maintain growth or water retention. In contrast, mild to moderate salt stress resulted in more balanced gene expression, reduced physiological damage, and improved plant development. These findings provide insights into the molecular and physiological responses of perennial ryegrass to different salt sources and may support future research on improving salinity tolerance in forage species.

Micronutrient malnutrition is a global health concern, highlighting the importance of essential micronutrients like zinc in human diet, and enhancing zinc concentration in staple crops like common beans is a promising strategy. In this study, we performed genome-wide association studies to identify genetic loci associated with Zn accumulation in a diverse panel of 177 Turkish common bean landraces and six commercial varieties. Field trials were conducted across two locations over two years to assess Zn concentration variation. The results revealed significant genetic variability, with Zn concentrations ranging from 11.6 to 105.3 mg/kg. Two significant marker-trait associations (MTAs) were identified on chromosomes Pv06 and Pv08, with loci linked to Zn translocation and brassinosteroid-mediated cell-wall remodeling. Candidate genes, Vacuolar Iron Transporter 1 (VIT1) and Wall-Associated Kinase-Like 4 (WAKL4) , were implicated in Zn homeostasis and distribution. In silico transcriptomics analysis further validated the role of these genes under Zn-deficient conditions in Arabidopsis. Protein-protein interaction (PPI) network analysis identified key regulatory pathways, including ZIP transporters, heavy metal ATPases, and cell-wall modification enzymes. These findings provide valuable genetic insights for biofortification strategies, facilitating the development of Zn-enriched common bean cultivars to enhance nutritional security. The study underscores the potential of GWAS in identifying stable genetic markers for Zn biofortification, paving the way for future breeding efforts aimed at improving dietary Zn intake through staple crops.

Orchardgrass ( Dactylis glomerata L.), a widely cultivated cool-season perennial, is an important forage crop due to its adaptability, high nutritional value, and substantial biomass. Understanding its genetic diversity and population structure is crucial for developing resilient cultivars that can withstand climate change, diseases, and resource limitations. Despite its global significance in fodder production, the genetic potential of many regional accessions remains unexplored, limiting breeding efforts. This study investigates the genetic diversity (GD) and population structure of 91 accessions of D. glomerata from Turkey and Iran using genotyping-by-sequencing based single nucleotide polymorphism (SNP) markers. A total of 2913 high-quality SNP markers revealed substantial genetic variability across provinces. Notably, accessions from Erzurum exhibited the highest GD (mean GD: 0.26; He: 0.5328), while provinces such as Bursa and Muğla demonstrated lower GD (mean GD: 0.15; He < 0.22), suggesting potential genetic bottlenecks. Population structure analysis using Bayesian clustering, PCoA and UPGMA dendrograms divided the accessions into three distinct clusters, with cluster membership largely reflecting geographical origins, and dry biomass content. Cluster II revealed higher GD, associated with enhanced biomass production (128 g/plant), the most important agronomic trait in forage species, supporting the notion of heterosis in breeding programs. The majority of the genetic variation (85.8%) was observed within clusters, with minimal differentiation among clusters (FST = 0.007). Genome-wide association studies (GWAS) identified significant marker-trait associations for dry biomass weight, a critical agronomic trait, with markers DArT-100715788, DArT-101043591, and DArT-101171265 and DArT-101090822 located on Chromosomes 1, 6, and 7 respectively. These findings highlight the importance of regional diversity for maintaining adaptive potential in future breeding programs.

White maize ( Zea mays L.) is increasingly valued for diversified food uses, yet agronomic performance and nutritional quality can fluctuate markedly across humid temperate seasons. This study evaluated 14 white maize testcross genotypes, including the commercial check P2948W, across five consecutive field seasons (2020-2024) in the Black Sea Region of Türkiye. Phenology and plant architecture, grain yield (t ha -1 ), major compositional traits (protein, oil, starch, cellulose and ash), and macro- and microelement concentrations (Ca, Mg, K, P, Fe, Zn, Cu and Mn) were assessed using near-infrared spectroscopy (NIRS) and standard field protocols. Data were analyzed using linear mixed-effects models to partition genotype, year, and genotype x year effects, followed by multivariate visualization (genotype x trait, GT, biplot) and stability assessment using AMMI and GGE biplot approaches (with years treated as environments). Grain yield showed wide genotypic variation (7.23-11.43 t ha-1), with P2948W ranking highest and TTBYM2019–37 lowest on the across-year mean basis, whereas pollen shedding occurred within a narrower window (73.7-78.1 days after planting). In contrast, most compositional traits and mineral means exhibited limited genotypic separation in the combined analysis, indicating strong seasonal influence on quality and mineral expression. Overall, the combined mixed-model and stability framework supports evidence-based selection of high-biomass, broadly adapted white maize candidates for regional cultivar development and provides a transparent basis for multi-year evaluation of quality and mineral attributes.