Erucic acid, more than 2 %, in mustard seed oil is considered unhealthy as edible oil, and also anti-nutritional for human consumption. The existing mustard varieties of Bangladesh contain 40–48 % erucic acid, which is a big concern for the country's nutritional, and food security and safety. Hence, to improve the seed oil quality of the existing variety, six popular cultivars of Brassica juncea mustard were crossed with a canola-grade line in 7 × 7 half diallel fashion, and the developed 21 F₁ hybrids were assessed for yield contributing traits, and fatty acids composition. Variables with significant variations were found, while days to siliquae maturity, plant height, days to first flowering, and seeds per siliquae have moderate narrow sense heritability. The estimated gene action indicated that dominant or over-dominant gene action was more prominent in governing the traits. The parents, P₁, P₃, and P₄ were discovered the best general combiners for early maturity and short phenology, whereas P₂ and P₇ were found to be the best general combiners for yield-attributing traits. Moreover, the hybrids P₁ × P₄, P₁ × P_6, P₂ × P₇, P₄ × P₆ and P₃ × P₅ were chosen as the promising hybrids due to their best specific combining ability, and desired heterotic effects on yield contributing traits. In addition, a significant decrease, on average 30–40 %, in erucic acid, but an approximately 20–25 % increase of oleic acid was found among the hybrids, in which the hybrids P₁ × P₆–S1, P₅ × P₆–S2 and P₅ × P₆–S4 demonstrated a better stability index. Overall, the obtained findings suggested that the hybrids, viz. P₁ × P₅, P₁ × P₆, P₂ × P₃, P₂ × P₇, P₄ × P₆, P₅ × P₆, and P₆ × P₇ were promising based on their early maturity, high-yielding, reduced erucic acid, and high oleic acid contents.

Legume–microbiome symbiotic interactions sustain ecosystem balance by enhancing grain production and soil health. However, this beneficial interaction is greatly altered by the adverse effects of biotic and abiotic stressors. The microbial compositions in different ecological niches demonstrated considerable functional diversity and host-specific stress responses. High-throughput metagenomic sequencing approaches uncover that microbes possess genes responsible for osmolyte biosynthesis, phytohormone modulation, and antioxidant production, providing great resilience against stress. Moreover, metagenomics opens a key avenue of integrating multi-omics tools such as metatranscriptomics, metaproteomics, and metabolomics that uncovers the mechanistic functions of the potential biosynthetic gene clusters that regulate host phenotypic expressions under stresses. However, the genomic data scarcity of many wild or cultivated legumes, standard protocols, and metadata accessibility greatly hinders the unlocking of the full potential of metagenomic approaches in revealing the hidden mechanisms of legume–microbiome associations. Coupling genomic approaches to bioinformatics, AI modules, and real-time validation of laboratory data at field levels are the prime scopes to harness the legume–microbiome association mapping and develop a predictive framework for stress-resilient legume breeding programs. This chapter elucidates the mechanisms of metagenomics to enhance comprehension of gene functions and metabolites that confer stress-responsive microbial traits to facilitate the advancement of climate-smart legume systems.