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

Abstract

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.

Genotype-environment interaction and stability of fiber properties and growth traits in triploid hybrid clones of Populus tomentosa

“…The stability values for the FL of the triploid hybrid clones were different for the different methods. This could be partially explained by the different principles that are used to estimate the stability parameters [ 24 – 27 ]. High values of the Finlay-Wilkinson stability parameter, i.e., the regression coefficient of the clone values on location value, determine the clones that benefit from a productive location, while high intercept coefficients determine the clones that can grow well under limited resource conditions.…”

Oat mega‐environments in Canada

“…Yan, Hunt, Sheng, and Szlavnics (2000) developed GGE (genotypic main effect [G] plus GE) biplot analysis, one of the utilities being to graphically show the which‐won‐where pattern. The use of GGE biplot analysis has since been adopted by numerous researchers in ME analysis for different regions and crops (Badu‐Apraku et al., 2011; Dardanelli et al., 2006; Dehghani, Ebadi, & Yousefi, 2006; Kaya, Akçura, & Taner, 2006; Luo et al., 2015; Mohammadi, Haghparast, Amri, & Ceccarelli, 2010; Munaro et al., 2014; Rakshit et al., 2012; Swanckaert et al., 2020; Voltas, Lopez‐Corcoles, & Borras, 2005; Xu, Fok, Zhang, LI, & Zhou, 2014). However, these studies had to use the strategy of “analyze yearly and summarize across years” (DeLacy, Basford, Cooper, Bull, & McLaren, 1996).…”

Genetic variability and genotype by environment interaction of two major cassava processed products in multi-environments

“…The resulting table of Best Linear Unbiased Estimates (BLUEs) was used to model the GEI using three approaches shown in Table 2; Malosetti et al (2013) The first approach was suggested by Finlay and Wilkinson (1963) (FW), a regression analysis which has been widely used to describe stability and GEI in various cultivars (Mulusew et al, 2014;Swanckaert et al, 2020). The Finlay-Wilkinson regression model estimates the heterogeneity of slopes and sensitivity of a genotype by regressing mean phenotypic performance of individual genotypes on an environmental index using withinline ordinary least squares (OLS) regression (Lian and de los Campos, 2016).…”