A Multi-Model Based Stability Analysis Employing Multi-Environmental Trials (METs) Data for Discerning Heat Tolerance in Chickpea (Cicer arietinum L.) Landraces

Abstract

Identifying a congenially targeted production environment and understanding the effects of genotype by environmental interactions on the adaption of chickpea genotypes is essential for achieving an optimal yield stability. Different models like additive main effect and multiplicative interactions (AMMI 1, AMM2), weighted average absolute scores of BLUPs (WAASB), and genotype plus genotype–environment (GGE) interactions were used to understand their suitability in the precise estimation of variance and their interaction. Our experiment used genotypes that represent the West Asia–North Africa (WANA) region. This trial involved two different sowing dates, two distinct seasons, and three different locations, resulting in a total of 12 environments. Genotype IG 5871(G1) showed a lower heat susceptibility index (HSI) across environments under study. The first four interactions principal component axis (IPCA) explain 93.2% of variations with significant genotype–environment interactions. Considering the AMMI stability value (ASV), the genotypes IG5862(G7), IG5861(G6), ILC239(G40), IG6002(G26), and ILC1932(G39), showing ASV scores of 1.66, 1.80, 2.20, 2.60, and 2.84, respectively, were ranked as the most stable and are comparable to the weighted average absolute scores of BLUPs (WAASB) ranking of genotypes. The which–won–where pattern of genotype plus genotype–environment (GGE) interactions suggested that the target environment consists of one mega environment. IG5866(G10), IG5865(G9), IG5884(G14), and IG5862(G7) displayed higher stability, as they were nearer to the origin. The genotypes that exhibited a superior performance in the tested environments can serve as ideal parental lines for heat-stress tolerance breeding programs. The weighted average absolute scores of BLUPs (WAASB) serve as an ideal tool to discern the variations and identify the stable genotype among all methods.

Dissection of the Genetic Basis of Genotype by Environment Interactions for Morphological Traits and Protein Content in Winter Wheat Panel Grown in Morocco and Spain

“…Therefore, more advanced statistical techniques than the conventional ANOVA are needed for a thorough assessment of GE interactions. Based on the GGE biplot analysis, a Mega-Environment (ME) can be determined when different genotypes are adapted to distinct groups of environments and the variance between groups is higher than within groups [ 23 , 24 , 25 , 37 , 40 ]. In our study, the different environments tested were clustered into two MEs for TKW and three for grain length and width.…”

“…Regarding width, the best-performing genotypes were 117, 64, and 183 for ME1, ME2, and ME3, respectively (Figure 8D and Table S4). [23][24][25][26], and across different environments, an ideal genotype is demonstrating both high stability and a superior average performance. The ranking of a genotype's performance is determined by its position close to the center of the concentric circles towards an absolute stable point (Average Environment Coordinate (AEC)).…”

Genotype-by-environment interaction and stability analysis of grain yield of bread wheat (Triticum aestivum L.) genotypes using AMMI and GGE biplot analyses

“…This approach, as mentioned by Refs. [ [143] , [144] , [145] ], allows plant breeders to evaluate and identify genotypes that exhibit stability in yield performance across diverse environmental conditions.…”