Publication:
Response and yield stability of canola (brassica napus l.) genotypes to multi-environments using gge biplot analysis

dc.contributor.authorAcar, Mustafa
dc.contributor.authorGizlenci, Sahin
dc.contributor.authorAtagun, Gulhan
dc.contributor.authorSuzer, Sami
dc.contributor.buuauthorUlusoy, Yahya
dc.contributor.buuauthorULUSOY, YAHYA
dc.contributor.buuauthorSincik, Mehmet
dc.contributor.buuauthorSİNCİK, MEHMET
dc.contributor.buuauthorSenyigit, Emre
dc.contributor.buuauthorŞENYİĞİT, EMRE
dc.contributor.buuauthorGoksoy, Abdurrahim T.
dc.contributor.buuauthorGÖKSOY, ABDURRAHİM TANJU
dc.contributor.departmentMustafakemal Paşa Yüksekokulu
dc.contributor.departmentTarla Bitkileri Ana Bilim Dalı
dc.contributor.orcid0000-0002-0012-4412
dc.contributor.orcid0000-0001-8641-6995
dc.contributor.orcid0000-0003-2658-3905
dc.contributor.researcheridAAH-1811-2021
dc.date.accessioned2024-06-14T08:07:19Z
dc.date.available2024-06-14T08:07:19Z
dc.date.issued2021-01-01
dc.description.abstractThe GxE interaction (GEI) provides essential information for selecting and recommending cultivars in multi-environment trials. This study aimed to evaluate genotype (G) and environment (E) main effects and GxE interaction of 15 canola genotypes (10 canola lines and 5 check varieties) over 8 environments and to examine the existence of different mega environments. Canola yield performances were evaluated during 2015/16 and 2016/17 production season in three different locations (Southern Marmara, Thrace side of Marmara, and Black Sea regions) of Turkey. The trial in each location was arranged in a randomized complete block design with four replications. The seed yield data were analyzed using GGE biplot and the yield components data were analyzed using ANOVA. The agronomical traits revealed that environments, genotypes, and GEI were significant at 1 % probability for all of the characters. The variance analysis exhibited that genotypes, environments, and GEI explained 21.6, 21.7, and 25.7 % of the total sum of squares for seed yield, respectively. The GGE biplot analysis showed that the first and second principal components explained 57.3 and 18.3 % of the total variation in the data matrix, respectively. GGE biplot analysis showed that the polygon view of a biplot is an excellent way to visualize the interactions between genotypes and environments.
dc.identifier.doi10.51372/bioagro332.4
dc.identifier.endpage114
dc.identifier.issn1316-3361
dc.identifier.issue2
dc.identifier.startpage105
dc.identifier.urihttps://doi.org/10.51372/bioagro332.4
dc.identifier.urihttps://hdl.handle.net/11452/42200
dc.identifier.volume33
dc.identifier.wos000645192800003
dc.indexed.wosWOS.SCI
dc.language.isoen
dc.publisherUniv Centroccidential Lisandro Alvarado
dc.relation.journalBioagro
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectCultivar evaluation
dc.subjectTrial data
dc.subjectGenotype x environment interaction
dc.subjectMulti-environment trials
dc.subjectSeed yield
dc.subjectYield components
dc.subjectScience & technology
dc.subjectLife sciences & biomedicine
dc.subjectAgriculture, multidisciplinary
dc.subjectAgronomy
dc.subjectAgriculture
dc.titleResponse and yield stability of canola (brassica napus l.) genotypes to multi-environments using gge biplot analysis
dc.typeArticle
dspace.entity.typePublication
local.contributor.departmentZiraat Fakültesi/Tarla Bitkileri Ana Bilim Dalı
local.contributor.departmentMustafakemal Paşa Yüksekokulu
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relation.isAuthorOfPublication124ec24a-9919-48c6-af6c-3af490821ade
relation.isAuthorOfPublicationb8df27e7-199c-4626-99ea-5850ed66c28c
relation.isAuthorOfPublication.latestForDiscovery1c7a2371-0a45-4eca-ba65-589d03a62d53

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