Enhanced Bayesian model for multienvironmental selection of winter hybrids maize: assessing grain yield using...

Area of Science:

• Agricultural Science
• Genetics and Breeding

Background:

• Phenotypic plasticity and crossover interactions complicate hybrid maize selection for optimal grain yield.
• A region-wide investigation in Nepal assessed 45 hybrid maize varieties over two years.
• The study aimed to identify site-specific and widely adapted hybrids for improved agricultural outcomes.

Purpose of the Study:

• To identify elite hybrid maize varieties with superior grain yield and consistent performance.
• To disclose site-specific and wide-adapted hybrids for diverse environmental conditions in Nepal.
• To streamline the hybrid maize selection process using advanced analytical methods.

Main Methods:

• Utilized the "ProbBreed" package for Bayesian probability analysis.
• Employed randomized complete block designs with three replicated trials at each testing station.
• Conducted a two-year, region-wide investigation across Nepal involving 45 hybrid maize varieties.

Main Results:

• Substantial genetic, environmental, and interactive influences on grain yield were identified (p < 0.05).
• DKC9149 (8.8 tons/ha) and NK6607 (8.6 tons/ha) emerged as elite hybrids with high probability coefficients.
• Several hybrids, including RMH1899 super, RMH 666, and Uttam 121, demonstrated strong performance across various environmental conditions and locations.

Conclusions:

• Selected hybrids are predicted to exhibit superior performance within their recommended domains.
• Integrating genomic information with Bayesian models is expected to enhance prediction accuracy.
• The study provides valuable insights for accelerating breeding progress and improving maize cultivation in Nepal.