Identification of mega‐environments for grain sorghum in Brazil using GGE biplot methodology

Abstract

The performance of genotypes in a wide range of environments can be affected by extensive genotype × environment (G × E) interactions, making the subdivision of the testing environments into relatively more homogeneous groups of locations (mega‐environments) a necessary strategy. The genotype main effects + genotype × environment interaction biplot method (GGE) allows identification of mega‐environments and selection of stable genotypes adapted to specific environments and mega‐environments. The objectives of this study were to identify mega‐environments regarding sorghum [Sorghum bicolor (L.) Moench] grain yield and demonstrate that the GGE biplot method can identify essential locations for conducting tests in different mega‐environments. A total of 22 competition trials of grain sorghum genotypes were conducted over three crop seasons across several production locations in Brazil. A total of 25, 22, and 30 genotypes were evaluated during the first, second, and third crop seasons, respectively. After identifying the presence of G × E interactions, the data were subjected to adaptability and stability analyses using the GGE biplot method. A phenotypic correlation network was used to express functional relationships between environments. The GGE biplot was found to be an efficient approach for identifying three mega‐environments in grain sorghum in Brazil, selecting representative and discriminative environments, and recommending more adaptive and stable grain sorghum genotypes.

Detection of Superior Rice Genotypes and Yield Stability under Different Nitrogen Levels Using AMMI Model and Stability Statistics

“…Accordingly, the genotypes G25, MTU1010, BG 304, and Yabani LuLu were the best genotypes having the lowest IPCAs values and located in close proximity to the origin. Using AMMI biplots, several investigations were able to identify their corresponding genotypes such as in barley [ 44 ], rice [ 40 ], Sorghum [ 45 ], cassava [ 46 ], and maize [ 47 ]. MTU1010 and Yabani LuLu are among the two groups most productive and stable.…”

Adaptability and stability of corn hybrids for silage via genotype and genotype × environment interaction biplot

“…Studies on the adaptability and stability of different crops based on biplots have proven the efficiency of this analysis to recommend genotypes and group favorable and unfavorable environments (G. O. Silva et al., 2011; K. J. Silva et al., 2021; Santos et al., 2016; Paramesh et al., 2016; Yokomizo et al., 2017; Olivoto et al., 2019; Albuquerque et al., 2022).…”

“…(D. P. D. Cruz et al., 2020; Santos, Ceccon, et al., 2017), soybean [ Glycine max (L.) Merr.] (Albuquerque et al., 2022), and sorghum [ Sorghum bicolor (L.) Moench] (Gomes et al., 2019; K. J. Silva et al., 2021), but there are few reports in the literature about its use in corn for silage production.…”

Using soybean historical field trial data to study genotype by environment variation and identify mega-environments with the integration of genetic and non-genetic factors

“…(2019) to identify two MEs in Midwestern Brazil with contrasting altitudes, levels of fertilizer, and incidence of soybean cyst nematode profiles. da S ilva et al . (2021) and K rishnamurthy et al .…”

“…2012), barley (A tlin et al . 2000b), wheat (G eorge and L undy 2019; B ustos -K orts 2017), sorghum ( da S ilva et al . 2021), alfafa (A nnicchiarico 2021), rice (K rishnamurthy et al .…”