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Genotype x environment interaction and yield stability of soybean (Glycine max l.) genotypes in multi-environment trials (METs) in Nigeria

dc.contributor.authorAbebe, A.T.
dc.contributor.authorAdewumi, A.S.
dc.contributor.authorAdebayo, M.A.
dc.contributor.authorShaahu, A.
dc.contributor.authorMushoriwa, H.
dc.contributor.authorAlabi, T.
dc.contributor.authorDerera, J.
dc.contributor.authorAgbona, A.
dc.contributor.authorChigeza, G.
dc.date.accessioned2025-04-30T08:07:56Z
dc.date.available2025-04-30T08:07:56Z
dc.date.issued2024-10-15
dc.identifier.citationAbebe, A.T., Adewumi, A.S., Adebayo, M.A., Shaahu, A., Mushoriwa, H., Alabi, T., ... & Chigeza, G. (2024). Genotype x environment interaction and yield stability of soybean (Glycine max l.) genotypes in multi-environment trials (METs) in Nigeria. Heliyon, 10(19): e38097, 1-15.
dc.identifier.issn2405-8440
dc.identifier.urihttps://hdl.handle.net/20.500.12478/8667
dc.description.abstractGenotype × environment interaction (GEI) poses a critical challenge to plant breeders by complicating the identification of stable variety (ies) for performance across diverse environments. GGE biplot and AMMI analyses have been identified as the most effective and appropriate statistical techniques for identifying stable and high-performing genotypes across diverse environments. The objective of this study was to identify widely adapted and high-yielding soybean genotypes from Multi-Locational Trials (MLTs) using GGE and AMMI biplot analyses. Fifteen IITA-bred elite soybean lines and three standard checks were evaluated for stability of performance in a 3 × 6 alpha lattice design with three replications across seven locations in Nigeria. Significant (p < 0.001) differences were detected among genotypes, environments, and GEI for grain yield, which ranged between 979.8 kg ha−1 and 3645 kg ha−1 with a mean of 2324 kg ha−1. To assess the stability of genotypes, analyses were conducted using the general linear method, GGE, and the Additive Main Effect and Multiplicative Interaction (AMMI) approach, as well as WAAS and ASV rank indices. In the GGE biplot model, the first two principal components accounted for 67.4 % of the total variation, while in the AMMI model, the first two Interaction Principal Component Axes (IPCA1 and IPCA2) explained 73.20 % and 11.40 % of the variation attributed to genotype by environment interaction, respectively. GGE biplot identified G10 and G16 as the most stable and productive genotypes, while WAASB index revealed G16, G10, G9, G4 and G2 as the most adaptive, stable and productive genotypes across locations, and ASV identified G9, G13, G4, G14 and G10 as the most stable and productive. Consequently, genotypes G2, G4, G9, G10 and G16 displayed outstanding and stable grain yield performance across the test locations and are, therefore, recommended for release as new soybean varieties suitable for cultivation in the respective mega environment where they performed best. More importantly, the two genotypes are recommended for recycling as sources of high-yield and yield stability genes, and as parental lines for high-yield and stable performance for future breeding and genomic selection.
dc.format.extent1-15
dc.language.isoen
dc.subjectGenotype-Environment Interaction
dc.subjectYields
dc.subjectSoybeans
dc.subjectVarieties
dc.subjectNigeria
dc.titleGenotype x environment interaction and yield stability of soybean (Glycine max l.) genotypes in multi-environment trials (METs) in Nigeria
dc.typeJournal Article
cg.contributor.crpGrain Legumes
cg.contributor.affiliationInternational Institute of Tropical Agriculture
cg.contributor.affiliationAjayi Crowther University
cg.contributor.affiliationNational Cereals Research Institute, Nigeria
cg.coverage.regionAfrica
cg.coverage.regionWest Africa
cg.coverage.countryNigeria
cg.coverage.hubHeadquarters and Western Africa Hub
cg.researchthemeBiotech and Plant Breeding
cg.isijournalISI Journal
cg.authorship.typesCGIAR and developing country institute
cg.iitasubjectAgronomy
cg.iitasubjectFood Security
cg.iitasubjectGrain Legumes
cg.iitasubjectPlant Breeding
cg.iitasubjectPlant Production
cg.iitasubjectSoybean
cg.journalHeliyon
cg.notesOpen Access Journal
cg.accessibilitystatusOpen Access
cg.reviewstatusPeer Review
cg.usagerightslicenseCreative Commons Attribution 4.0 (CC BY 0.0)
cg.targetaudienceScientists
cg.identifier.doiUnited States Agency for International Development
cg.iitaauthor.identifierAbush Tesfaye: 0000-0002-9245-360X
cg.iitaauthor.identifierAdeyinka Adewumi: 0000-0002-5981-1882
cg.iitaauthor.identifierTunrayo Alabi: 0000-0001-5142-6990
cg.iitaauthor.identifierJohn Derera: 0000-0003-3715-0689
cg.iitaauthor.identifierAFOLABI AGBONA: 0000-0002-9756-5432
cg.iitaauthor.identifierGodfree Chigeza: 0000-0002-9235-0694
cg.futureupdate.requiredNo
cg.identifier.issue19: e38097
cg.identifier.volume10


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