{"title":"Enhanced Bayesian model for multienvironmental selection of winter hybrids maize: assessing grain yield using 'ProbBreed'.","authors":"Bikas Basnet, Chitra Bahadur Kunwar, Umisha Upreti","doi":"10.1186/s13007-025-01327-2","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"8"},"PeriodicalIF":4.7000,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11776175/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Plant Methods","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1186/s13007-025-01327-2","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍：
Plant Methods is an open access, peer-reviewed, online journal for the plant research community that encompasses all aspects of technological innovation in the plant sciences.
There is no doubt that we have entered an exciting new era in plant biology. The completion of the Arabidopsis genome sequence, and the rapid progress being made in other plant genomics projects are providing unparalleled opportunities for progress in all areas of plant science. Nevertheless, enormous challenges lie ahead if we are to understand the function of every gene in the genome, and how the individual parts work together to make the whole organism. Achieving these goals will require an unprecedented collaborative effort, combining high-throughput, system-wide technologies with more focused approaches that integrate traditional disciplines such as cell biology, biochemistry and molecular genetics.
Technological innovation is probably the most important catalyst for progress in any scientific discipline. Plant Methods’ goal is to stimulate the development and adoption of new and improved techniques and research tools and, where appropriate, to promote consistency of methodologies for better integration of data from different laboratories.
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