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Performance of extraearly maize cultivars based on GGE biplot and AMMI analysis

dc.contributor.authorBadu-Apraku, B.
dc.contributor.authorOyekunle, M.
dc.contributor.authorObeng-Antwi, K.
dc.contributor.authorOsuman, A.S.
dc.contributor.authorAdo, S.G.
dc.contributor.authorCoulibaly, N.
dc.contributor.authorYallou, C.G.
dc.contributor.authorAbdulai, M.S.
dc.contributor.authorBoakyewaa, G.A.
dc.contributor.authorDidjeira, A.
dc.date.accessioned2019-12-04T11:10:14Z
dc.date.available2019-12-04T11:10:14Z
dc.date.issued2012-08
dc.identifier.citationBadu-Apraku, B., Oyekunle, M., Obeng-Antwi, K., Osuman, A.S., Ado, S.G., Coulibay, N., ... & Didjeira, A. (2012). Performance of extra-early maize cultivars based on GGE biplot and AMMI analysis. Journal of Agricultural Science, 150(4), 473-483.
dc.identifier.issn0021-8596
dc.identifier.urihttps://hdl.handle.net/20.500.12478/2031
dc.descriptionPublished online: 05 October 2011
dc.description.abstractMulti-environment trials (METs) in West Africa have demonstrated the existence of genotype×environment interactions (G×E), which complicate the selection of superior cultivars and the best testing sites for identifying superior and stable genotypes. Two powerful statistical tools available for MET analysis are the additive main effects and multiplicative interaction (AMMI) and the genotype main effect+G×E (known as GGE) biplot. The objective of the present study was to compare their effectiveness in identifying maize mega-environments and stable and superior maize cultivars with good adaptation to West Africa. Twelve extra-early maturing maize cultivars were evaluated at 17 locations in four countries in West Africa from 2006 to 2009. The effects of genotype (G), environments (E) and G×E were significant (P<0 01) for grain yield. Differences between E accounted for 0 75 of the total variation in the sum of squares for grain yield, whereas the G effects accounted for 0 03 and G×E for 0 22. The GGE biplot explained 0 74 of total variations in the sum of squares for grain yield and revealed three mega-environments and seven cultivar groups. The AMMI graph explained 0 13 and revealed four groups each of environments and cultivars. The two procedures provided similar results in terms of stability and performance of the cultivars. Both methods identified the cultivars 2004 TZEE-W Pop STR C4 and TZEE-W Pop STR C4 as superior across environments. Cultivar 2004 TZEE-W Pop STR C4 was the most stable. The GGE biplot was more versatile and flexible, and provided a better understanding of G×E than the AMMI graph. It identified Zaria, Ilorin, Ikenne, Ejura, Kita, Babile, Ina and Angaredebou as the core testing sites of the three mega-environments for testing the Regional Uniform Variety Trials-extra-early.
dc.description.sponsorshipUnited States Agency for International Development
dc.format.extent473-483
dc.language.isoen
dc.subjectMaize
dc.subjectCultivars
dc.subjectGenotypes
dc.subjectAmmi
dc.subjectMulti-Environment Trials
dc.subjectGenotype × Environment Interactions
dc.titlePerformance of extraearly maize cultivars based on GGE biplot and AMMI analysis
dc.typeJournal Article
dc.description.versionPeer Review
cg.contributor.crpGrain Legumes
cg.contributor.crpMaize
cg.contributor.affiliationInternational Institute of Tropical Agriculture
cg.contributor.affiliationCouncil for Scientific and Industrial Research, Ghana
cg.contributor.affiliationInstitute for Agricultural Research, Nigeria
cg.contributor.affiliationInstitut d'Economie Rurale, Mali
cg.contributor.affiliationInstitut National des Recherches Agricoles du Bénin
cg.contributor.affiliationSavanna Agricultural Research Institute, Ghana
cg.contributor.affiliationInstitu Togolais de Recherches Agricoles
cg.coverage.regionAfrica
cg.coverage.regionWest Africa
cg.coverage.countryNigeria
cg.isijournalISI Journal
cg.iitasubjectGenetic Improvement
cg.iitasubjectMaize
cg.iitasubjectPlant Genetic Resources
cg.journalJournal of Agricultural Science
cg.howpublishedFormally Published
cg.accessibilitystatusLimited Access
local.dspaceid85870
cg.targetaudienceScientists
cg.identifier.doihttp://dx.doi.org/10.1017/s0021859611000761


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