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Parsimonious genotype by environment interaction covariance models for cassava (Manihot esculenta)

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.authorJannink, J.
dc.contributor.authorRabbi, I.Y.
dc.date.accessioned2023-04-18T08:10:22Z
dc.date.available2023-04-18T08:10:22Z
dc.date.issued2022-09-21
dc.identifier.citationBakare, M.A., Kayondo, S.I., Aghogho, C.I., Wolfe, M., Parkes, E., Kulakow, P., ... & Rabbi, I.Y. (2022). Parsimonious genotype by environment interaction covariance models for cassava (Manihot esculenta). Frontiers in Plant Science, 13: 978248, 1-18.
dc.identifier.issn1664-462X
dc.identifier.urihttps://hdl.handle.net/20.500.12478/8138
dc.description.abstractThe assessment of cassava clones across multiple environments is often carried out at the uniform yield trial, a late evaluation stage, before variety release. This is to assess the differential response of the varieties across the testing environments, a phenomenon referred to as genotype-by-environment interaction (GEI). This phenomenon is considered a critical challenge confronted by plant breeders in developing crop varieties. This study used the data from variety trials established as randomized complete block design (RCBD) in three replicates across 11 locations in different agro-ecological zones in Nigeria over four cropping seasons (2016–2017, 2017–2018, 2018–2019, and 2019–2020). We evaluated a total of 96 varieties, including five checks, across 48 trials. We exploited the intricate pattern of GEI by fitting variance–covariance structure models on fresh root yield. The goodness-of-fit statistics revealed that the factor analytic model of order 3 (FA3) is the most parsimonious model based on Akaike Information Criterion (AIC). The three-factor loadings from the FA3 model explained, on average across the 27 environments, 53.5% [FA (1)], 14.0% [FA (2)], and 11.5% [FA (3)] of the genetic effect, and altogether accounted for 79.0% of total genetic variability. The association of factor loadings with weather covariates using partial least squares regression (PLSR) revealed that minimum temperature, precipitation and relative humidity are weather conditions influencing the genotypic response across the testing environments in the southern region and maximum temperature, wind speed, and temperature range for those in the northern region of Nigeria. We conclude that the FA3 model identified the common latent factors to dissect and account for complex interaction in multi-environment field trials, and the PLSR is an effective approach for describing GEI variability in the context of multi-environment trials where external environmental covariables are included in modeling.
dc.description.sponsorshipBill & Melinda Gates Foundation
dc.description.sponsorshipCGIAR Trust Fund
dc.description.sponsorshipForeign, Commonwealth and Development Office, United Kingdom
dc.format.extent1-18
dc.language.isoen
dc.subjectCassava
dc.subjectGenotypes
dc.subjectGenotype Environment Interaction
dc.subjectFood Security
dc.subjectNigeria
dc.titleParsimonious genotype by environment interaction covariance models for cassava (Manihot esculenta)
dc.typeJournal Article
cg.contributor.crpRoots, Tubers and Bananas
cg.contributor.affiliationCornell University
cg.contributor.affiliationInternational Institute of Tropical Agriculture
cg.coverage.regionAfrica
cg.coverage.regionWest Africa
cg.coverage.countryNigeria
cg.coverage.hubHeadquarters and Western Africa Hub
cg.researchthemeBiotech and Plant Breeding
cg.researchthemePlant Production and Health
cg.identifier.bibtexciteidBAKARE:2022a
cg.isijournalISI Journal
cg.authorship.typesCGIAR and advanced research institute
cg.iitasubjectAgronomy
cg.iitasubjectCassava
cg.iitasubjectFood Security
cg.iitasubjectPlant Breeding
cg.iitasubjectPlant Production
cg.journalFrontiers in Plant Science
cg.notesOpen Access Journal; Published online: 21 Sep 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/10.3389/fpls.2022.978248
cg.iitaauthor.identifierKayondo Siraj Ismail: 0000-0002-3212-5727
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.issue978248
cg.identifier.volume13


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