article · Heliyon
Bread wheat production faces challenges from environmental variability and yield reductions linked to poor adaptation. Twelve bread wheat genotypes were evaluated across three environments over two years to assess genotype-by-environment interactions and yield stability using AMMI and GGE biplot analyses. Grain yield was significantly influenced by environment, genotype, and their interaction. Genotype G7 emerged as the most stable and high-yielding variety overall, demonstrating minimal performance fluctuation across test sites. Meanwhile, the Tay genotype, G6, produced the highest grain yield in the best-performing test environment and was identified as an ideal genotype balancing high yield and broad adaptability. Genotypes G5 and G10 also demonstrated high stability scores. These assessments provide clear criteria for categorising breeding lines into broadly adaptable or environment-specific candidates.
Wheat is a critical global staple crop, yet environmental constraints frequently lead to unpredictable yields. Pinpointing which genotypes remain reliably productive across fluctuating conditions, and which excel in specific regions, helps plant breeders target their efforts. This knowledge supports the development of robust, climate-resilient wheat crops capable of securing consistent harvests under diverse farming environments.
This research provides applied and tested performance data for wheat breeders, seed multiplication enterprises, and agricultural researchers. The identified genotypes, such as G6 and G7, represent advanced breeding lines that could be carried forward into formal national variety release programmes and commercial seed production. The work serves as an intermediate step between experimental breeding trials and market-ready commercial seed varieties suitable for specific target farming zones.
AI-generated from the published abstract. Always read the original work before citing.
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.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.1016/j.heliyon.2024.e32918
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.