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Variance component estimations and mega-environments for sweetpotato breeding in West Africa.

Jolien SwanckaertDaniel AkansakeKwadwo AdofoKwabena AcheremuBert De BoeckRaul EyzaguirreWolfgang J GrünebergJan W LowHugo Campos
Published in: Crop science (2020)
The current study was aimed at identifying mega-environments in Ghana and evaluating adaptability of superior sweetpotato [Ipomoea batatas (L.) Lam.] genotypes from a targeted breeding effort. Three sets of genotypes were evaluated in multi-environment trials (MET). Twelve sweetpotato varieties were evaluated across nine environments representing the main agro-ecological zones in Ghana. MET analysis was conducted using a stage-wise approach with the genotype × environment (G × E) table of means used as a starting point to model the G × E interaction for sweetpotato yield. Emphasis was given to the genetic correlation matrix used in a second-order factor analytic model that accommodates heterogeneity of genetic variances across environments. A genotype main effect and G × E interaction of storage root yield explained 82% of the variation in the first principal component, and visualized the genetic variances and discriminating power of each environment and the genetic correlation between the environments. Two mega-environments, corresponding to northern and southern trial sites, were delineated. Six breeding lines selected from the south and eight breeding lines selected from the north were tested and compared to two common check clones at five locations in Ghana. A Finlay-Wilkinson stability analysis resulted in stable performances within the target mega-environment from which the genotypes were selected, but predominantly without adaptation to the other region. Our results provide a strong rationale for running separate programs to allow for faster genetic progress in each of these two major West African mega-environments by selecting for specific and broad adaptation.
Keyphrases
  • genome wide
  • copy number
  • clinical trial
  • study protocol
  • climate change
  • risk assessment
  • single cell