Cereal Research Communications

, Volume 44, Issue 4, pp 628–638 | Cite as

A Comparison of Winter Wheat Cultivar Rankings in Groups of Polish Locations

  • A. Derejko
  • M. StudnickiEmail author
  • W. Mądry
  • E. Gacek


The grouping of locations from local-scale multi-environmental trials (METs) into mega-environments has been criticized. Some European countries, e.g. the Czech Republic, Poland and Germany, have been characterized as possessing homogeneous environmental conditions. For aligned environmental conditions, it has been assumed that cultivar rankings will be similar and consequently cannot be used to designate mega-environments. An example of METs at the local scale is the Polish Post Registration Variety Testing System. The objective of this study was to determine groups of test sites within 16 Polish regions which are characterized by similar yield ranking of 50 winter wheat cultivars over three growing seasons (2011–2013). The compatibility of these cultivar yield rankings across regions was evaluated using Pearson correlation coefficients. Thereby, the 16 regions were divided into six groups (mega-environments) of locations. Regions within each group have similar cultivar rankings, whereas between groups, we observed different cultivar rankings, indicating crossover interactions. Besides similar cultivar yield responses the regions within mega-environments were characterized also by similar environmental (soil and/or climate) conditions.


adaptation G×E interaction mega-environment multi-environmental trial Triticum aestivum yield 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Supplementary material

42976_2016_4404628_MOESM1_ESM.pdf (166 kb)
A Comparison of Winter Wheat Cultivar Rankings in Groups of Polish Locations


  1. Annicchiarico, P., Bellah, F., Chiari, T. 2005. Defining subregions and estimating benefits for a specific-adaptation strategy by breeding programs: A case study. Crop Sci. 45:1741–1749.CrossRefGoogle Scholar
  2. Atlin, G.N., Baker, R.J., McRae, K.B., Lu, X. 2000. Selection response in subdivided target regions. Crop Sci. 40:7–13.CrossRefGoogle Scholar
  3. Atlin, G.N., McRae, K.B. 1994. Resource allocation in Maritime cereal cultivar trials. Can. J. Plant Sci. 74:501–505.CrossRefGoogle Scholar
  4. Burgueño, J., Crossa, J., Cotes, J.M., Vicente, F.S., Das, B. 2011. Prediction assessment of linear mixed models for multi-environment trials. Crop Sci. 51:944–954.CrossRefGoogle Scholar
  5. Barrero Farfan, I.D., Murray, S.C., Labar, S., Pietsch, D. 2013. A multi-environment trial analysis shows slight grain yield improvement in Texas commercial maize. Field Crop Res. 149:167–176.CrossRefGoogle Scholar
  6. Ebdon, J.S., Gauch, H.G. 2002. Additive main effect and multiplicative interactions analysis of national turf-grass performance trials. Interpretation of genotype×environment interactions. Crop Sci. 42:489–496.Google Scholar
  7. Federer, W.T., King, F. 2007. Variations on Split Plot and Split Block Experiment Designs. John Wiley and Sons. New York, USA.CrossRefGoogle Scholar
  8. Gauch, H.G., Zobel, R.W. 1997. Identifying mega-environments and targeting genotypes. Crop Sci. 37:311–326.CrossRefGoogle Scholar
  9. Gilmour, A.R., Gogel, B.J., Cullis, B.R., Thompson, R. 2009. ASReml User Guide Release 3.0. VSN International Ltd., Hemel Hempstead, UK.Google Scholar
  10. Hu, X., Yan, S., Shen, K. 2013. Heterogeneity of error variance and its influence on genotype comparison in multi-location trials. Field Crop Res. 149:322–328.CrossRefGoogle Scholar
  11. Kelly, A.M., Smith, A.B., Eccleston, J.A., Cullis, B.R. 2007. The accuracy of varietal selection using factor analytic models for multi-environment plant breeding trials. Crop Sci. 47:1063–1070.CrossRefGoogle Scholar
  12. Liu, S.M., Constable, G.A., Reid, P.E., Stiller, W.N., Cullis, B.R. 2013. The interaction between breeding and crop management in improved cotton yield. Field Crop Res. 148:49–60.CrossRefGoogle Scholar
  13. Mądry, W., Paderwski, J., Gozdowski, D., Rozbici, J., Golba, J., Piechociński, M., Studnicki, M., Derejko, A. 2013. Adaptation of winter wheat cultivars to crop managements and Polish agricultural environments. Turkish J. Field Crop 18:118–127.Google Scholar
  14. Mandal, N.P., Sinha, P.K., Variar, M., Shukla, V.D., Perraju, P., Mehta, A., Pathak, A.R., Dwivedi, J.L., Rathi, S.P.S., Bhandarkar, S., Singh, B.N., Singh, D.N., Panda, S., Mishra, N.C., Singh, Y.V., Pandya, R. 2010. Implications of genotype×input interactions in breeding superior genotypes for favourable and unfavourable rainfed upland environments. Field Crop Res. 118:135–144.CrossRefGoogle Scholar
  15. Mohammadi, R., Roustaii, M., Haghparast, R., Roohi, E., Solimani, K., Ahmadi, M., Abedi, R., Amri, A. 2010. Genotype × environment interactions for grain yield in rainfed winter multi-environment trials in Iran. Agron. J. 102:1500–1510.CrossRefGoogle Scholar
  16. Möhring, J., Piepho, H.P. 2009. Comparison of weighting in two-stage analyses of series of experiments. Crop Sci. 49:1977–1988.CrossRefGoogle Scholar
  17. Munaro, L.B., Benin, G., Marchioro, V.S., de Assis Franco, F., Silva, R.R., de Silva, C.L., Beche, E. 2014. Brazilian spring wheat homogeneous adaptation regions can be dissected in major mega-environments. Crop Sci. 54:1374–1383.CrossRefGoogle Scholar
  18. Mühleisen, J., Piepho, H.P., Maurer, H.P., Zhao, Y., Reif, J.C. 2014. Exploitation of yield stability in barley. Theor. Appl. Genet. 127:1949–1962.CrossRefGoogle Scholar
  19. Patterson, H.D., Thompson, R. 1971. Recovery of inter-block information when block sizes are unequal. Biometrika 58:545–554.CrossRefGoogle Scholar
  20. Piepho, H.P., Möhring, J., Schulz-Streeck, T., Ogutu, J.O. 2012. A stage-wise approach for analysis of multi-environment trials. Biom. J. 54:844–860.CrossRefGoogle Scholar
  21. Seber, G.A.F. 2004. Multivariate Observations. John Wiley and Sons. New York, USA.Google Scholar
  22. Smith, A.B., Cullis, B.R., Thompson, R. 2001. Analyzing variety by environment data using multiplicative mixed models and adjustments for spatial field trend. Biometrics 57:1138–1147.CrossRefGoogle Scholar
  23. Smith, A.B., Cullis, B.R., Thompson, R. 2005. The analysis of crop cultivar breeding and evaluation trials: an overview of current mixed model approaches. J. Agric. Sci. 143:449–462.CrossRefGoogle Scholar
  24. Studnicki, M., Mądry, W., Derejko, A., Noras, K., Wójcik-Gront, E. 2015. Four-way data analysis within the linear mixed modelling framework. Sci. Agric. 72:411–419.CrossRefGoogle Scholar
  25. Tapley, M., Ortiz, B.V., van Santen, E., Balkcom, K.S., Mask, P., Weaver, D.B. 2013. Location, seeding date, and variety interactions on winter wheat yield in South-eastern United States. Agron. J. 105:509–518.CrossRefGoogle Scholar
  26. Welham, S.J., Cullis, B.R., Gogel, B.J., Gilmour, A.R., Thompson, R. 2004. Prediction in linear mixed models. Aust. NZ. J. Stat. 46:325–347.CrossRefGoogle Scholar
  27. Wu, H.X., Matheson, A.C. 2004. General and specific combining ability from partial diallels of radiata pine: implications for utility of SCA inbreeding and deployment populations. Theor. Appl. Genet. 108:1503–1512.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest 2016

Authors and Affiliations

  1. 1.Department of Experimental Design and BioinformaticsWarsaw University of Life SciencesWarsawPoland
  2. 2.Research Centre for Cultivar Testing (COBORU)Słupia WielkaPoland

Personalised recommendations