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dc.contributor.authorUkalski, Krzysztof
dc.contributor.authorKlisz, Marcin
dc.date.accessioned2016-12-21T21:09:36Z
dc.date.available2016-12-21T21:09:36Z
dc.date.issued2016-12-21
dc.identifier.otherdoi 10.1515/ffp-2016-0026
dc.identifier.urihttps://depot.ceon.pl/handle/123456789/11009
dc.description.abstractIn the studies on selection and population genetics of forest trees that include the analysis of genotype × environment interaction (GE), the use of biplot graphs is relatively rare. This article describes the models and analytic methods useful in the biplot graphs, which enable the analyses of mega-environments, selection of the testing environment, as well as the evaluation of genotype stability. The main method presented in the paper is the GGE biplot method (G – genotype effect, GE –genotype × environment interaction effect). At the same time, other methods have also been referred to, such as, SVD (singular value decomposition), PCA (principal component analysis), linear-bilinear SREG model (sites regression), linear-bilinear GREG model (genotypes regression) and AMMI (additive main effects multiplicative interaction). The potential of biplot method is presented based on the data on growth height of 20 European beech genotypes (Fagus sylvatica L.), generated from real data concerning selection trials and carried out in 5 different environments. The combined ANOVA was performed using fixed-effects, as well as mixed-effects models, and significant interaction GE was shown. The GGE biplot graphs were constructed using PCA. The first principal component (GGE1) explained 54%, and the second (GGE2) explained more than 23% of the total variation. The similarity between environments was evaluated by means of the AEC method, which allowed us to determine one mega-environment that comprised of 4 environments. None of the tested environments represented the ideal one for trial on genotype selection. The GGE biplot graphs enabled: (a) the detection of a stable genotype in terms of tree height (high and low), (b) the genotype evaluation by ranking with respect to the height and genotype stability, (c) determination of an ideal genotype, (d) the comparison of genotypes in 2 chosen environments.pl_PL
dc.language.isoplpl_PL
dc.publisherThe Forest Sciences and Committee on Forestry Sciences and Wood Technology of the Polish Academy of Sciences; Instytut Badawczy Lesnictwa (Forest Research Institute), Sekocin Stary, Polandpl_PL
dc.rightsCreative Commons Uznanie autorstwa na tych samych warunkach 3.0 Polska
dc.rights.urihttp://creativecommons.org/licenses/by-sa/3.0/pl/legalcode
dc.subjectSVD singular value decompositionpl_PL
dc.subjectPCA principal component analysispl_PL
dc.subjectmulti-environment trialpl_PL
dc.subjectgenotype × environment interactionpl_PL
dc.subjectGGE biplot analysispl_PL
dc.subjectAMMI additive main effects multiplicative interactionpl_PL
dc.titleApplication of GGE biplot graphs in multi-environment trials on selection of forest treespl_PL
dc.typeinfo:eu-repo/semantics/articlepl_PL
dc.contributor.organizationWarsaw University of Life Sciences – SGGW, Faculty of Applied Informatics and Mathematics, Department of Econometrics and Statistics, Biometry Divisionpl_PL
dc.contributor.organizationForest Research Institute, Department of Silviculture and Genetics, Sękocin Starypl_PL
dc.description.epersonPrzemysław Szmit
dc.rights.DELETETHISFIELDinfo:eu-repo/semantics/openAccess


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Creative Commons Uznanie autorstwa na tych samych warunkach 3.0 Polska
Except where otherwise noted, this item's license is described as Creative Commons Uznanie autorstwa na tych samych warunkach 3.0 Polska