Enhanced Bayesian model for multienvironmental selection of winter hybrids maize: assessing grain yield using ‘ProbBreed’ - Bikas Basnet; Chitra Bahadur Kunwar; Umisha Upreti (Plant Methods)
Abstract Background Crossover interactions stemming from phenotypic plasticity complicate selection decisions when evaluating hybrid maize with superior grain yield and consistent performance. Consequently, a two-year, region-wide investigation of 45 hybrids maize across Nepal was performed with the aim of disclosing both site and wide adapted hybrids. Utilizing an innovative “ProbBreed” package, based on Bayesian probability analysis of randomized complete block designs with three replicated trials at each station, this study substantively streamlines hybrids maize selection. Results This finding revealed substantial genetic, environmental, and interactive influences on grain yield (p < 0.05). Among the hybrids, DKC9149 (8.8 tons/ha) emerged as the elite with probability coefficient of (0.39), followed by NK6607(0.35 & 8.6 tons/ha). Joint probability analysis identified RMH1899 super (0.23 & 8.3 tons/ha), followed by RMH 666 (0.15 & 8.4 tons/ha) and Uttam 121 (0.11 & 8.6 tons/ha), all of which accounted for overall environmental conditions. Additionally, over the years, DKC 9149, NK 6607(0.18 & 8.6 tons/ha), GK 3254(0.18 & 8.5 tons/ha), Shann 111(0.12 & 8.4 tons/ha), Sweety 1(0.13 & 8.4 tons/ha), and ADV 756(0.10 & 8.2 tons/ha) consistently demonstrated superior performance and stability. Delving with site specific recommendations include Nepalgunj: RMH 9999(8.5 tons/ha), NK 6607(8.6 tons/ha); Parwanipur: DKC 9149, MM 2033(8.5 tons/ha); Rampur: ADV 756, DKC 9149, MM 2929(8.6 tons/ha); and Tarahara: GK 3254(8.5 tons/ha), NK 6607(8.6 tons/ha), Uttam 121. Conclusion Thus, Selected hybrids are predicted to outperform within the recommended domain. Over and above, integrating genomic information into Bayesian models expected to enhance prediction accuracy and expedite breeding progress.