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

Abstract

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.

References

  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.

    Article  Google 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.

    Article  Google Scholar 

  3. Atlin, G.N., McRae, K.B. 1994. Resource allocation in Maritime cereal cultivar trials. Can. J. Plant Sci. 74:501–505.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    Book  Google Scholar 

  8. Gauch, H.G., Zobel, R.W. 1997. Identifying mega-environments and targeting genotypes. Crop Sci. 37:311–326.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google Scholar 

  16. Möhring, J., Piepho, H.P. 2009. Comparison of weighting in two-stage analyses of series of experiments. Crop Sci. 49:1977–1988.

    Article  Google 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.

    Article  Google 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.

    Article  Google Scholar 

  19. Patterson, H.D., Thompson, R. 1971. Recovery of inter-block information when block sizes are unequal. Biometrika 58:545–554.

    Article  Google 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.

    Article  Google 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.

    CAS  Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google Scholar 

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Correspondence to M. Studnicki.

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Communicated by H. Grausgruber

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Derejko, A., Studnicki, M., Mądry, W. et al. A Comparison of Winter Wheat Cultivar Rankings in Groups of Polish Locations. CEREAL RESEARCH COMMUNICATIONS 44, 628–638 (2016). https://doi.org/10.1556/0806.44.2016.029

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Keywords

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