AI Article Synopsis

  • - In Bangladesh, sweet potato ranks as the fourth most important carbohydrate source but suffers from issues of yield and stability, prompting researchers to employ various selection methods to identify better-performing genotypes that maintain quality traits.
  • - Through multiple selection methods and a multi-environment trial, researchers identified 71 superior sweet potato genotypes from an initial pool of 351, eventually narrowing it down to 11 top-performing genotypes to optimize yield and quality.
  • - Utilizing advanced statistical models like linear mixed models and the WAASBY index, the study determined that specific genotypes (H20, H37, H8, and H41) excelled in key metrics, such as marketable root yield and nutritional content, indicating a reliable selection

Article Abstract

In Bangladesh, sweet potato holds the fourth position as a crucial carbohydrate source, trailing rice, wheat, and potato. However, locally grown sweet potato varieties often display limited stability and yield. To tackle this challenge, diverse selection methods and statistical models were utilized to pinpoint sweet potato genotypes showcasing both stability and superior yield and quality traits. In the initial two years, multiple selection methods were employed to narrow down the collections based on preferences for yield and its contributing traits. Subsequently, a multi-environment trial (MET) was conducted in the following year to pinpoint superior and stable genotypes with desirable yield and quality characteristics. An integrated approach involving the Multi-Trait Genotype Ideotype Distance Index (MGIDI), Factor Analysis and Ideotype-Design (FAI-BLUP), and Smith-Hazel Index (SH) led to the identification of 71 superior sweet potato genotypes out of a total of 351 in the initial growing season. In the subsequent season, the MGIDI selection index was applied to the 71 genotypes, resulting in the selection of 11 top-performing genotypes. This selection process was complemented by a detailed analysis of the strengths and weaknesses of the selected genotypes. In the MET, the mixed effect model, specifically the linear mixed model (LMM), identified significant genotypic and genotype-environment interaction (GEI) variances. This points to elevated heritability and selection accuracy, ultimately boosting the model's reliability. By combining the strengths of LMM and additive main effects and multiplicative interaction (AMMI), the best linear unbiased prediction (BLUP) index identified H20 as the top-performing genotype for marketable root yield (MRY), H37 for dry weight of root (DW), H8 for beta carotene (BC) and H41 for vitamin c (VC). These genotypes surpassed the overall average in the WAAS index. For simultaneous stability and high performance, the WAASBY index selected H37 for MRY, H6 for DW, H61 for BC, and H3 for VC. Finally, genotypes H3 and H20 were selected using multi-trait stability index (MTSI), as they possessed high performance and stability. Based on the selection sense, the objective has been achieved with regards to the trait MRW, which serves as a major criterion for a superior variety of sweet potato.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11141454PMC
http://dx.doi.org/10.1016/j.heliyon.2024.e31569DOI Listing

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