Enhanced Bayesian model for multienvironmental selection of winter hybrids maize: assessing grain yield using ‘ProbBreed’ DOI Creative Commons
Bikas Basnet,

Chitra Bahadur Kunwar,

Umisha Upreti

et al.

Plant Methods, Journal Year: 2025, Volume and Issue: 21(1)

Published: Jan. 29, 2025

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 across Nepal was performed the aim disclosing both site wide adapted hybrids. Utilizing an innovative "ProbBreed" package, based on Bayesian probability analysis randomized complete block designs three replicated trials at each station, this study substantively streamlines selection. This finding revealed substantial genetic, environmental, interactive influences (p < 0.05). Among hybrids, DKC9149 (8.8 tons/ha) emerged as elite coefficient (0.39), followed by NK6607(0.35 & 8.6 tons/ha). Joint identified RMH1899 super (0.23 8.3 tons/ha), RMH 666 (0.15 8.4 Uttam 121 (0.11 all which accounted for overall environmental conditions. Additionally, over years, DKC 9149, NK 6607(0.18 GK 3254(0.18 8.5 Shann 111(0.12 Sweety 1(0.13 ADV 756(0.10 8.2 consistently demonstrated performance stability. Delving specific recommendations include Nepalgunj: 9999(8.5 6607(8.6 tons/ha); Parwanipur: MM 2033(8.5 Rampur: 756, 2929(8.6 Tarahara: 3254(8.5 121. Thus, Selected are predicted to outperform within recommended domain. Over above, integrating genomic information into models expected enhance prediction accuracy expedite breeding progress.

Language: Английский

Enhanced Bayesian model for multienvironmental selection of winter hybrids maize: assessing grain yield using ‘ProbBreed’ DOI Creative Commons
Bikas Basnet,

Chitra Bahadur Kunwar,

Umisha Upreti

et al.

Plant Methods, Journal Year: 2025, Volume and Issue: 21(1)

Published: Jan. 29, 2025

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 across Nepal was performed the aim disclosing both site wide adapted hybrids. Utilizing an innovative "ProbBreed" package, based on Bayesian probability analysis randomized complete block designs three replicated trials at each station, this study substantively streamlines selection. This finding revealed substantial genetic, environmental, interactive influences (p < 0.05). Among hybrids, DKC9149 (8.8 tons/ha) emerged as elite coefficient (0.39), followed by NK6607(0.35 & 8.6 tons/ha). Joint identified RMH1899 super (0.23 8.3 tons/ha), RMH 666 (0.15 8.4 Uttam 121 (0.11 all which accounted for overall environmental conditions. Additionally, over years, DKC 9149, NK 6607(0.18 GK 3254(0.18 8.5 Shann 111(0.12 Sweety 1(0.13 ADV 756(0.10 8.2 consistently demonstrated performance stability. Delving specific recommendations include Nepalgunj: 9999(8.5 6607(8.6 tons/ha); Parwanipur: MM 2033(8.5 Rampur: 756, 2929(8.6 Tarahara: 3254(8.5 121. Thus, Selected are predicted to outperform within recommended domain. Over above, integrating genomic information into models expected enhance prediction accuracy expedite breeding progress.

Language: Английский

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