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Exploring genotype by environment interaction on cassava yield and yield related traits using classical statistical methods

dc.contributor.authorBakare, M.A.
dc.contributor.authorKayondo, S.I.
dc.contributor.authorAghogho, C.I.
dc.contributor.authorWolfe, M.
dc.contributor.authorParkes, E.
dc.contributor.authorKulakow, P.
dc.contributor.authorEgesi, C.
dc.contributor.authorRabbi, I.Y.
dc.contributor.authorJannink, J.
dc.date.accessioned2022-08-09T12:51:02Z
dc.date.available2022-08-09T12:51:02Z
dc.date.issued2022
dc.identifier.citationBakare, M.A., Kayondo, S.I., Aghogho, C.I., Wolfe, M., Parkes, E., Kulakow, P., ... & Jannink, J. (2022). Exploring genotype by environment interaction on cassava yield and yield related traits using classical statistical methods. PloS One, 17(7): e026818, 1-24.
dc.identifier.issn1932-6203
dc.identifier.urihttps://hdl.handle.net/20.500.12478/7619
dc.description.abstractVariety advancement decisions for root quality and yield-related traits in cassava are complex due to the variable patterns of genotype-by-environment interactions (GEI). Therefore, studies focused on the dissection of the existing patterns of GEI using linear-bilinear models such as Finlay-Wilkinson (FW), additive main effect and multiplicative interaction (AMMI), and genotype and genotype-by-environment (GGE) interaction models are critical in defining the target population of environments (TPEs) for future testing, selection, and advancement. This study assessed 36 elite cassava clones in 11 locations over three cropping seasons in the cassava breeding program of IITA based in Nigeria to quantify the GEI effects for root quality and yield-related traits. Genetic correlation coefficients and heritability estimates among environments found mostly intermediate to high values indicating high correlations with the major TPE. There was a differential clonal ranking among the environments indicating the existence of GEI as also revealed by the likelihood ratio test (LRT), which further confirmed the statistical model with the heterogeneity of error variances across the environments fit better. For all fitted models, we found the main effects of environment, genotype, and interaction significant for all observed traits except for dry matter content whose GEI sensitivity was marginally significant as found using the FW model. We identified TMS14F1297P0019 and TMEB419 as two topmost stable clones with a sensitivity values of 0.63 and 0.66 respectively using the FW model. However, GGE and AMMI stability value in conjunction with genotype selection index revealed that IITA-TMS-IBA000070 and TMS14F1036P0007 were the top-ranking clones combining both stability and yield performance measures. The AMMI-2 model clustered the testing environments into 6 mega-environments based on winning genotypes for fresh root yield. Alternatively, we identified 3 clusters of testing environments based on genotypic BLUPs derived from the random GEI component.
dc.description.sponsorshipBill & Melinda Gates Foundation
dc.description.sponsorshipUnited Kingdom’s Foreign, Commonwealth & Development Office
dc.format.extent1-24
dc.language.isoen
dc.subjectCassava
dc.subjectVarieties
dc.subjectGenotypes
dc.subjectFood Security
dc.subjectFood Crops
dc.subjectNigeria
dc.titleExploring genotype by environment interaction on cassava yield and yield related traits using classical statistical methods
dc.typeJournal Article
cg.contributor.crpRoots, Tubers and Bananas
cg.contributor.affiliationCornell University
cg.contributor.affiliationInternational Institute of Tropical Agriculture
cg.contributor.affiliationUniversity of Ghana
cg.contributor.affiliationNational Root Crops Research Institute, Nigeria
cg.contributor.affiliationUnited States Department of Agriculture
cg.coverage.regionAfrica
cg.coverage.regionWest Africa
cg.coverage.countryNigeria
cg.coverage.hubHeadquarters and Western Africa Hub
cg.researchthemeBiotech and Plant Breeding
cg.identifier.bibtexciteidBAKARE:2022
cg.isijournalISI Journal
cg.authorship.typesCGIAR and developing country institute
cg.iitasubjectAgronomy
cg.iitasubjectCassava
cg.iitasubjectFood Security
cg.iitasubjectGenetic Improvement
cg.iitasubjectPlant Breeding
cg.iitasubjectPlant Genetic Resources
cg.iitasubjectPlant Production
cg.journalPLoS ONE
cg.notesOpen Access Journal; Published online: 18 Jul 2022
cg.accessibilitystatusOpen Access
cg.reviewstatusPeer Review
cg.usagerightslicenseCreative Commons Attribution 4.0 (CC BY 0.0)
cg.targetaudienceScientists
cg.identifier.doihttps://dx.doi.org/10.1371/journal.pone.0268189
cg.iitaauthor.identifierE J Parkes: 0000-0003-4063-1483
cg.iitaauthor.identifierPeter Kulakow: 0000-0002-7574-2645
cg.iitaauthor.identifierChiedozie Egesi: 0000-0002-9063-2727
cg.iitaauthor.identifierIsmail Rabbi: 0000-0001-9966-2941
cg.futureupdate.requiredNo
cg.identifier.issue7: e026818
cg.identifier.volume17


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