Morphological Variation in Normal Maize Landrace Accessions Collected from South Sudan

Emmanuel Andrea Mathiang, Kyu Jin Sa, Hyeon Park, So Jung Jang, Ju Kyong Lee

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Morphological variation was investigated among 31 maize landrace accessions collected from the fields of various farmers in South Sudan by examining 7 quantitative agronomic characteristics. A significant positive correlation was found between days to tasseling (DT) and days to silking (DS), between plant height (PH) and ear height (EH) and stem width (SW), and between EH and SW and leaf length (LL). First and second principal components accounted for 69% of the total variance (40.9% and 28.6%, respectively). PH, EH, SW, LL, and leaf width (LW) were clearly characterized in a positive direction on the first axis. DT and DS were clearly characterized in a positive direction on the second axis. A scatter plot based on phenotypic data revealed the existence of 3 groups based on the most discriminating characteristics: Group I included 5 maize landrace accessions, Group II comprised 14 maize landrace accessions, and Group III included 10 maize landrace accessions. In principal component analysis, generally the investigated genotypes were not clearly grouped into their geographical origins owing to a weak geographic relationship among the accessions. In conclusion, even though the morphological characterization studies were conducted in the South Korean climate, this study revealed significant phenotypic variation among the explored maize landrace accessions collected from South Sudan. Therefore, this information about phenotypic divergence may be very useful for future breeding research programs as well as for genetic improvement of South Sudan maize accessions.

Original languageEnglish
Pages (from-to)15-24
Number of pages10
JournalPlant Breeding and Biotechnology
Volume11
Issue number1
DOIs
StatePublished - 2023

Keywords

  • Correlation coefficient
  • Landrace
  • Maize
  • Morphological variation
  • Principal component analysis
  • Quantitative traits

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