Advertisement

Cereal Research Communications

, Volume 43, Issue 4, pp 692–703 | Cite as

Evaluation of Spring Barley Performance by Biplot Analysis

  • N. Pržulj
  • M. MirosavljevićEmail author
  • P. Čanak
  • M. Zorić
  • J. Boćanski
Breeding

Abstract

Unpredictable environmental conditions lead to occurrence of large genotype by environment (G × E) interaction. It reduces the correlation between genotypic and phenotypic values and complicates selection of superior genotypes. The objective of this study was to estimate genotype by year (G × Y) interaction using AMMI model, to identify spring barley genotypes with stable and high yield performance and to observe association of different meteorological variables with tested growing seasons. The trials with 15 spring barley genotypes were conducted during seven years (1999–2005) at the location of Rimski Šančevi. The results showed that the influence of year (Y), genotype (G) and G × Y interaction on barley grain yield were significant (p < 0.01). Meteorological variables varied significantly from year to year and Y explained the highest percent of treatment variation (81%). The first three IPCA were significant and explained 83% of interaction variation. According to this study, it could be concluded that AMMI analysis provided an enhanced understanding of G × Y interaction in barley multi-years trials. Among the tested genotypes, LAV and NS 477 could be separated as highest yielding genotypes, however LAV could be recommended for further breeding program and large-scale production due to its stable and high yielding performance. It also provided better insight in specific association between spring barley grain yield and meteorological variables.

Keywords

AMMI analysis genotype by year interaction grain yield Hordeum vulgare L. meteorological variables 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgements

This paper presents the results of the project TR-31066 “Modern breeding of small grains for present and future needs”, supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia.

References

  1. Annicchiarico, P. 1997. Additive main effects and multiplicative interaction (AMMI) of genotype-location interaction in variety trials repeated over years. Theor. Appl. Genet. 94:1072–1077.CrossRefGoogle Scholar
  2. Calderini, D.F., Savin, R., Labeled, O.G., Reynolds, M.P., Slafer, G.A. 2001. The importance of the period immediately preceding anthesis for grain weight determination in wheat. Euphytica 119:199–204.CrossRefGoogle Scholar
  3. Ceccarelli, S. 1989. Wide adaptation. How wide? Euphytica 40:197–205.Google Scholar
  4. Ceretta, S., van Eeuwijk, F. 2008. Grain yield variation in malting barley cultivars in Uruguay and its consequences for the design of a trials network. Crop Sci. 48:167–180.CrossRefGoogle Scholar
  5. FAOSTAT 2012. Food and Agriculture Organization (FAO): FAOSTAT. (Available at http://faostat.fao.org)
  6. Garcia del Moral, L.F., Rharrabti, Y., Villegas, D., Royo, C. 2003. Evaluation of grain yield and its components in durum wheat under Mediterranean conditions: an ontogenic approach. Agron. J. 95:266–274.CrossRefGoogle Scholar
  7. Gauch, G.H., Zobel, R.W. 1996. AMMI analysis of yield trials. In: Kang, M.S., Gauch, H.G. (eds), Genotype by environment interaction. CRC Press. Boca Raton, FL, USA. pp. 85–122.CrossRefGoogle Scholar
  8. Kaya, Y., Palta, C., Taner, S. 2002. Additive main effects and multiplicative interactions analysis of yield performances in bread wheat genotypes across environments. Turk. J. Agric. For. 26:275–279.Google Scholar
  9. Kilic, H., Sagir, A., Bayram, Y. 2009. Estimates of genotype x environment interactions and heritability of black point in durum wheat. Not. Sci. Biol. 1:92–96.CrossRefGoogle Scholar
  10. Lipkovich, I., Smith, E.P. 2002. Biplot and singular value decomposition macros for Excel. J. Stat. Softw. 7:1–15.CrossRefGoogle Scholar
  11. Metzger, M.J., Bunce, R.G.H., Jongman, R.H.G., Mücher, C.A., Watkins, J.W. 2005. A climatic stratification of Europe. Global Ecol. Biogeogr. 14:549–563.CrossRefGoogle Scholar
  12. Mitrović, B., Stanisavljević, D., Treskić, S., Stojaković, M., Ivanović, M., Bekavac, G., Rajković, M. 2012. Evaluation of experimental maize hybrids tested in multilocation trials using AMMI and GGE biplot analyses. Turk. J. Field Crops. 17:35–40.Google Scholar
  13. Mladenov, V., Banjac, B., Krishna, A., Milošević, M. 2012. Relation of grain protein content and some agronomic traits in European cultivars of winter wheat. Cereal Res. Commun. 40:532–541.CrossRefGoogle Scholar
  14. Olesen, J.E., Trnka, M., Kersebaum, K.C., Skjelvag, A.O., Seguin, B., Peltonen-Sainio, P., Rossi, F., Kozyra, J., Micale, F. 2011. Impacts and adaptation of European crop production systems to climate change. Eur. J. Agron. 34:96–112.CrossRefGoogle Scholar
  15. Passarella, V.S., Savin, R., Slafer, G.A. 2008. Are temperature effects on weight and quality of barley grains modified by resource availability? Aust. J. Agric. Res. 59:510–516.CrossRefGoogle Scholar
  16. Pržulj, N., Momčilović, V. 2012. Spring barley performances in the Pannonian zone. Genetika 44:499–512.CrossRefGoogle Scholar
  17. Pržulj, N., Momčilović, V., Simić, J., Mirosavljević, M. 2014. Effect of growing season and variety on quality of spring two-rowed barley. Genetika 46:59–73.CrossRefGoogle Scholar
  18. Rao, P.S., Reddy, P.S., Rathore, A., Reddy, B.V., Panwar, S. 2011. Application GGE biplot and AMMI model to evaluate sweet sorghum (Sorghum bicolor) hybrids for genotype × environment interaction and seasonal adaptation. Ind. J. Agric. Sci. 81:438–844.Google Scholar
  19. Romagosa, I., Fox, P. 1993. Genotype × environment interaction and adaptation. In: Hayward M.D., Bosemark, N.O., Romagosa, I. (eds), Plant Breeding: Principles and Prospects. Chapman and Hall. London, UK. pp. 373–390.CrossRefGoogle Scholar
  20. Romagosa, I., van Eeuwijk, F.A., Thomas, W.T.B. 2009. Statistical analyses of genotype by environment data. In: Phohens, J., Nuez, F., Carena, M.J., (eds), Handbook of Plant Breeding. Elsevier. New York, NY, USA. pp. 1–41.Google Scholar
  21. Schelling, K., Born, K., Weissteiner, C., Kühbauch, W. 2003. Relationships between yield and quality parameters of malting barley (Hordeum vulgare L.) and phenological and meteorological data. J. Agron. Crop Sci. 189:113–122.CrossRefGoogle Scholar
  22. Shah, N.H., Paulsen, G.M. 2003. Interaction of drought and high temperature on photosynthesis and grainfilling of wheat. Plant Soil 257:219–226.CrossRefGoogle Scholar
  23. Sivapalan, S., Brien, L.O., Ferrara, G.O., Hollamby, G.L., Barclay, I, Martin, P.J. 2000. An adaptation analysis of Australian and CIMMYT/ICARDA wheat germplasm in Australian production environments. Aust. J Agric. Res. 51:903–915.CrossRefGoogle Scholar
  24. Slafer, G.A. 2003. Genetic basis of yield as viewed from a crop physiologist’s perspective. Ann. Appl. Biol. 142:117–128.CrossRefGoogle Scholar
  25. StatSoft, Inc. 2011. STATISTICA (data analysis software system), version 10 (http://www.statsoft.com).
  26. Ugarte, C., Calderini, D.F., Slafer, G.A. 2007. Grain weight and grain number responsiveness to pre-anthesis temperature in wheat, barley and triticale. Field. Crop. Res. 100:240–248.CrossRefGoogle Scholar
  27. Ullrich, S.E. 2011: Significance, adaptation, production, and, trade of barley. In: Ullrich, S.E., (ed.), Barley production, improvement, and uses. John Wiley & Sons Inc. Ames, IA, USA. pp. 3–13.Google Scholar
  28. Wardlaw, I.F., Dawson, I.A., Munibi, P., Fewster, R. 1989. The tolerance of wheat to high temperatures during reproductive growth. Survey procedures and general response patterns. Aust. J. Agric. Res. 40:1–13.Google Scholar
  29. Yan, W., Rajcan, I. 2002. Biplot analysis of test sites and trait relations of soybean in Ontario. Crop Sci. 42:11–20.CrossRefGoogle Scholar
  30. Zobel, R., Wright, M.J., Gauch, H.G. 1988. Statistical analysis of yield trial. Agron. J. 80:388–393.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest 2015

Authors and Affiliations

  • N. Pržulj
    • 1
  • M. Mirosavljević
    • 2
    Email author
  • P. Čanak
    • 2
  • M. Zorić
    • 2
  • J. Boćanski
    • 3
  1. 1.Faculty of AgriculturUniversity of Banja LukaBanja LukaBosnia and Herzegovina
  2. 2.Institute of Field and Vegetable CropsNovi SadSerbia
  3. 3.Faculty of AgricultureUniversity of Novi SadNovi SadSerbia

Personalised recommendations