Identification of Favourable Testing Locations for Barley Breeding in South Pannonian Plain
Abstract
The aim of this study was to identify desirable, and also non-informative or highly correlated locations using GGE
biplot. In this study, ten barley genotypes were tested across five locations for two growing seasons in official state trials
performed by the Ministry of Agriculture, Forestry, and Water Management of the Republic of Serbia. In both growing
seasons, environment had the highest influence on barley yield, explaining 77.70% in 2010/11 and 86.41% in 2011/12
growing season of the total variation. A significant grain yield variation explained by environmental effects indicated
that the environments tested in our study were highly diverse. Together, PC1 and PC2 amounted 86.03% and 66.91%
of the genotype and genotype × environment interaction sum of squares, in 2010/11 and 2011/12, respectively. The
results indicate that Rimski šančevi was most favorable location and should be used for further multi-location trials
while location Sremska Mitrovica was the least informative and it can be excluded from further trials. Excluding one
of two similar environments could save resources with minimal risk to lose important information about genotypes
performance. According to the results of our study, it can be concluded that GGE biplot is useful method for environment
evaluation.
Keywords
References
- Blanche S B & Myers G O (2006). Identifying discriminating locations for cultivar selection in Louisiana. Crop Science 46: 946-949
- Ding M, Tier B, Yan W, Wu H X, Powell M B & McRae T A (2008). Application of GGE biplot analysis to evaluate genotype (G), environment (E), and G×E interaction on Pinus radiata: A case study. New Zealand Journal of Forestry Science 38(1): 132-142
- Dogan Y, Kendal E & Oral E (2016). Identifying of relationship between traits and grain yield in spring barley by GGE biplot analysis. Agriculture & Forestry 62(4): 239-252
- Gabriel K R (1971). The biplot graphic display of matrices with application to principal component analysis. Biometrika 58: 453-467
- Kaya Y, Akcura M & Taner S (2006). GGE-Biplot analysis of multi-environment yield trials in bread wheat. Turkish Journal of Agriculture and Forestry 30: 325-337
- Kendal E & Dogan Y (2015). Stability of a candidate and cultivars (Hordeum vulgare L.) by GGE biplot analysis of multi-environment yield trials in spring barley. Agriculture and Forestry 61(4): 307-318
- Kendal E & Aktas H (2016). Investigation of genotypes by environment interaction using GGE biplot analysis in barley. Oxidation Communications, Biological and Biochemical Oxidation Proceses 39(3-1): 24-33
- Kendal E & Tekdal S (2016). Application of AMMI model for evolution spring barley genotypes in multienvironment trials. Bangladesh Journal of Botany 45(3): 613-620
Details
Primary Language
English
Subjects
Engineering
Journal Section
Research Article
Authors
Milan Mırosavljevıć
*
This is me
Serbia
Petar čanak
This is me
Serbia
Vojislava Momčılovıć
This is me
Serbia
Bojan Jockovıć
This is me
Serbia
Miroslav Zorıć
This is me
Serbia
Vladimir Aćın
This is me
Serbia
Srbislav Denčıć
This is me
Serbia
Novo Pržulj
This is me
Bosnia and Herzegovina
Publication Date
September 5, 2018
Submission Date
February 22, 2017
Acceptance Date
June 5, 2017
Published in Issue
Year 2018 Volume: 24 Number: 3
Cited By
Identification of Spring Barley Breeding Lines With Superior Yield Performance and Stability
Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis
https://doi.org/10.11118/actaun202068060947