Destaw Mullualem: Department of Biology, College of Natural and Computational Science, Injibara University, Injibara, Ethiopia.
Alemu Tsega: Department of Biology, College of Natural and Computational Science, Injibara University, Injibara, Ethiopia.
Tesfaye Mengie: Department of Biology, College of Natural and Computational Science, Injibara University, Injibara, Ethiopia.
Desalew Fentie: Department of Plant Science, College of Agriculture, Food and Climate Science, Injibara University, 40, Injibara, Ethiopia.
Zelalem Kassa: Department of Plant Science, College of Agriculture, Food and Climate Science, Injibara University, 40, Injibara, Ethiopia.
Amare Fassil: Department of Biology, College of Natural and Computational Science, Injibara University, Injibara, Ethiopia.
Demekech Wondaferew: Department of Plant Science, College of Agriculture, Food and Climate Science, Injibara University, 40, Injibara, Ethiopia.
Temesgen Assefa Gelaw: Department of Biotechnology, College of Agriculture and Natural Resource Sciences, Debre Birhan University, Debre Birhan, Ethiopia.
Tessema Astatkie: Faculty of Agriculture, Dalhousie University, Truro, Nova Scotia, Canada.
Bread wheat is a vital staple crop worldwide; including in Ethiopia, but its production is prone to various environmental constraints and yield reduction associated with adaptation. To identify adaptable genotypes, a total of 12 bread wheat genotypes (G1 to G12) were evaluated for their genotype-environment interaction (GEI) and stability across three different environments for two years using Additive Main Effect and Multiplicative Interaction (AMMI) and genotype main effect plus genotype-by-environment interaction (GGE) biplots analysis. GEI is a common phenomenon in crop improvement and is of significant importance in genotype assessment and recommendation. According to combined analysis of variance, grain yield was considerably impacted by environments, genotypes, and GEI. AMMI and GGE biplots analysis also provided insights into the performance and stability of the genotypes across diverse environmental conditions. Among the 12 genotypes, G6 was selected by AMMI biplot analysis as adaptive and high-yielding genotype; G5 and G7 demonstrated high stability and minimal interaction with the environment, as evidenced by their IPCA1 values. G7 was identified as the most stable and high-yielding genotype. The GGE biplot's polygon view revealed that the highest grain yield was obtained from G6 in environment three (E3). E3 was selected as the ideal environment by the GGE biplot. The top three stable genotypes identified by AMMI stability value (ASV) were G5, G7, and G10, while the most stable genotype determined by Genotype Selection Index (GSI) was G7. Even though G6 was a high yielder, it was found to be unstable according to ASV and ranked third in stability according to GSI. Based on the study's findings, the GGE biplot genotype view for grain yield identified Tay genotype (G6) to be the most ideal genotype due to its high grain yield and stability in diverse environments. G7 showed similar characteristics and was also stable. These findings provide valuable insights to breeders and researchers for selecting high-yielding and stable, as well as high-yielding specifically adapted genotypes.
