Identification of Favourable Testing Locations for Barley Breeding in South Pannonian Plain

Year 2018, Volume: 24 Issue: 3, 303 - 311, 05.09.2018

Milan Mırosavljevıć Petar čanak Vojislava Momčılovıć Bojan Jockovıć Miroslav Zorıć Vladimir Aćın Srbislav Denčıć Novo Pržulj

https://doi.org/10.15832/ankutbd.451279

Cited By: 2

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

Environment; GGE biplot; Interaction; Ideal environment

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
• Meng Y, Ren P, Ma X, Li B, Bao Q, Zhang H, Wang J, Bai J & Wang H (2016). GGE biplot-based evaluation of yield performance of barley genotypes across different environments in China. Journal of Agricultural Science and Technology 18: 533-543
• Mirosavljević M, Momčilović V, Pržulj N, Hristov N,Aćin V, Čanak P & Denčić S (2016). The variation of agronomic traits associated with breeding progress in winter barley cultivars. Zemdirbyste-Agriculture 103(3): 267-272
• Mitrović B, Stanisavljević D, Treskić S, Stojaković M, Ivanović M, Bekavac G & Rajković M (2012). Evaluation of experimental maize hybrids tested in multi-location trials using AMMI and GGE biplot analyses. Turkish Journal of Field Crops 17(1): 35-40
• Mortazavian S M M, Nikkhah H R, Hassani F A, Taheri M & Mahlooji M (2014). GGE biplot and ammi analysis of yield performance of barley genotypes across different environments in Iran. Journal of Agricultural Science and Technology 16(3): 609-622
• Pržulj N & Momčilović V (2012). Spring barley performances in the Pannonian zone. Genetika 44: 499-512
• Pržulj N, Mirosavljević M, Čanak P, Zorić M & Boćanski J (2015). Evaluation of spring barley performance by biplot analysis. Cereal Research Communications 43(4): 692-703
• Rakshit S, Ganapathy K N, Gomashe S S, Rathore A,Ghorade R B, Nagesh Kumar M V, Ganesmurthy K, Jain S K, Kamtar M Y, Sachan J S, Ambekar S S, Ranwa B R, Kanawade D G, Balusamy M, Kadam D, Sarkar A, Tonapi V A & Patil J V (2012). GGE biplot analysis to evaluate genotype, environment and their interactions in sorghum multi-location data. Euphytica 21(2): 145-156
• Sayar M S & Han Y (2016). Forage yield performance of forage pea (Pisum sativum spp. arvense L.) genotypes and assessments using gge biplot analysis. Journal of Agricultural Science and Technology 18: 1621-1634
• Stanisavljević D, Mitrović B, Mirosavljević M, Ćirić M, Čanak P, Stojaković M & Ivanović M (2013). Identification of the most desirable maize testing environments in northern Serbia. Field and Vegetable Crops Research 50: 28-35
• Stojaković M, Ivanović M, Bekavac G, Nastasić A, Purar B, Mitrović B & Stanisavljević D (2012). Evaluation of new NS maize hybrids using biplot analysis. Genetika 44: 1-12
• Tonk F A, Ilker E & Tosun M (2011). Evaluation of genotype × environment interactions in maize hybrids using GGE biplot analysis. Crop Breeding and Applied Biotechnology 11: 1-9
• Yan W & Tinker N A (2006). Biplot analysis of multienvironment trial data: Principles and applications. Canadian Journal of Plant Science 86: 623-645
• Yan W & Holland J B (2010). A heritability-adjusted GGE biplot for test environment evaluation. Euphytica 171(3): 355-369
• Yan W, Kang M S, Ma B, Woods S & Cornelius P L (2007). GGE biplot vs AMMI analysis of genotypeby-environment data. Crop Science 47: 643-655
• Yan W, Fregeau-Reid J, Pageau D, Martin R, Mitchell-Fetch J, Etienne M, Rowsell J, Scott P, Price M, De Haan B, Cummiskey A, Lajeunesse J, Durand J &Sparry E (2010). Identifying essential test locations for oat breeding in Eastern Canada. Crop Science 50:505-515
• Yan W, Pageau D, Frégeau-Reid J A & Durand J (2011). Assessing the representativeness and repeatability of test locations for genotype evaluation. Crop Science 51(4): 1603-1610
• Yan W, Frégeau-Reid J, Martin R, Pageau D & Mitchell-Fetch J (2015). How many test locations and replications are needed in crop variety trials for a target region? Euphytica 202(3): 361-372