Advertisement

Cereal Research Communications

, Volume 40, Issue 4, pp 592–601 | Cite as

Measures of genotype wide adaptation level and their relationships in winter wheat

  • W. MądryEmail author
  • M. Iwańska
Breeding

Abstract

Three new concepts of genotype wide adaptation levels I, II and III are presented and shown to the adequate describing quantitatively by measures such as superiority measure, Pi, Eskridge’s yield reliability measure, Ri and Eskridge’s yield reliability function, Ri(d). These indices have been called quantitative measures of genotype wide adaptation levels I, II and III, respectively. Relationships (consistency) between the three measures were studied using data for grain yield of winter wheat advanced lines from 15 preliminary multi-environment trials carried out across Polish test locations in the years 1993–2007. The quantitative measures are simple to interpret and useful quantitative characteristics of genotype wide adaptation levels I, II and III. High Spearman rank correlation coefficients were found between each of the pairs of the quantitative measures of genotype wide adaptation levels I, II and III within all sets of winter wheat genotypes. Then, for evaluating wheat genotype wide adaptation level in each aspect only one of the considered measures could be sufficient. The studies delivered new results on the usefulness of quantitative measures of genotype wide adaptation level for winter wheat. These findings indicate that those measures could be also useful for comparative evaluation of genotype wide adaptation level in other crops.

Keywords

genotype wide adaptation level grain yield preliminary multi-environment yield trials (MET) winter wheat quantitative measures 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Annicchiarico, P. 2002. Genotype-environment interactions: challenges and opportunities for plant breeding and cultivar recommendations. FAO Plant Production and Protection Paper No. 174. Food and Agriculture Organization, Rome.Google Scholar
  2. Baxevanos, D., Goulas, C., Tzortzios, S., Mavromatis, A. 2008. Interrelationship among and repeatability of seven stability indices estimated from commercial cotton (Gossypium hirsutum L.) variety evaluation trials in three Mediterranean countries. Euphytica 161:371–382.CrossRefGoogle Scholar
  3. Becker, H.C., Leon, J. 1988. Stability analysis in plant breeding. Plant Breeding 101:1–23.CrossRefGoogle Scholar
  4. Blanche, S.B., Utomo, H.S., Wenefrida, I., Myers, G.O. 2009. Genotype × environment interactions of hybrid and varietal rice cultivars for grain yield and milling quality. Crop Sci. 49:2011–2018.CrossRefGoogle Scholar
  5. Braun, H.J., Rajaram, S., van Ginkel, M. 1996. CIMMYT’s approach to breeding for wide adaptation. Euphytica 92:175–183.CrossRefGoogle Scholar
  6. Calimski, T., Czajka, S., Kaczmarek, Z. 1997. A multivariate approach to analysing genotype-environment interactions. In: Krajewski, P., Kaczmarek, Z (eds), Advances in Biometrical Genetics, Poznam, pp. 3–14.Google Scholar
  7. Ceccarelli, S. 1989. Wide adaptation: How wide? Euphytica 40:197–205.Google Scholar
  8. Cooper, M. 1999. Concepts and strategies for plant adaptation research in rainfed lowland rice. Field Crops Res. 64:13–34.CrossRefGoogle Scholar
  9. Cooper, M., Byth, D.E. 1996. Understanding plant adaptation to achieve systematic applied crop improvement — a fundamental challenge. In: Cooper, M., Hammer, G.L. (eds), Plant Adaptation, Crop Improvement. CAB International/International Rice Research Institute (IRRI)/International Crops Research Institute for Semi Arid Tropics (CRISAT), Wallingford, UK/Los Banos, Laguna, Philippines/Hyderabad, India, pp. 5–24.Google Scholar
  10. Duarte, J.B., Zimmermann, M.J. 1995. Correlation among yield stability parameters in common bean. Crop Sci. 35:905–912.CrossRefGoogle Scholar
  11. Eskridge, K.M. 1990. Selection of stable cultivars using a safety-first rule. Crop Sci. 30:369–374.CrossRefGoogle Scholar
  12. Eskridge, K.M., Byrne, P.F., Crossa, J. 1991. Selection of stable cultivars by minimizing the probability of disaster. Field Crops Res. 27:169–181.CrossRefGoogle Scholar
  13. Eskridge, K.M., Mumm, R.F. 1992. Choosing plant cultivars based on the probability of outperforming a check. Theor. Appl. Genet. 84:494–500.CrossRefGoogle Scholar
  14. Eskridge, K.M., Smith, O.S., Byrne, P.F. 1993. Comparing test cultivars using reliability functions of test-check differences from on-farm trials. Theor. Appl. Genet. 87:60–64.CrossRefGoogle Scholar
  15. Flores F., Moreno, M.T., Cubero, J.I. 1998. A comparison of univariate and multivariate methods to analyze G×E interaction. Field Crops Res. 56:271–286.CrossRefGoogle Scholar
  16. Gauch, H.G., Piepho, H.P., Annicchiarico, P. 2008. Statistical analysis of yield trials by AMMI and GGE: Further considerations. Crop Sci. 48:866–889.CrossRefGoogle Scholar
  17. Iwamska, M., Mbdry, W., Drzazga, T., Rajfura, A. 2008. Assessment of wide adaptation degree of winter wheat cultivars by statistical measures using yield data from preregistration trials. Biul. IHAR 250:67–86. (in Polish)Google Scholar
  18. Joshi, K.D., Musa, A.M., Johansen, C., Gyawali, S., Harris, D., Witcombe, J.R. 2007. Highly client-oriented breeding, using local preferences and selection, produces widely adapted rice cultivars. Field Crops Res. 100:107–116.CrossRefGoogle Scholar
  19. Kang, M.S. 1998. Using genotype-by environment interaction for crop cultivar development. Adv. Agron. 62:199–253.CrossRefGoogle Scholar
  20. Kang, M.S., Pham, H.N. 1991. Simultaneous selection for high and stable crop genotypes. Agron. J. 83:161–165.CrossRefGoogle Scholar
  21. Lin, C.S., Binns, M.R. 1988. A superiority measure of cultivar performance for cultivar × location data. Can. J. Plant Sci. 68:193–198.CrossRefGoogle Scholar
  22. Mbdry, W. 2003. Yield-stability statistic for selecting widely adapted genotypes of spring wheat and oat. J. New Seeds 5:43–56.CrossRefGoogle Scholar
  23. Mbdry, W., Kang, M.S. 2005. Scheffé-Calimski and Shukla models: Their interpretation and usefulness in stability and adaptation analyses. J. Crop Improv. 14:325–369.CrossRefGoogle Scholar
  24. Moghaddam, M.J., Pourdad, S.S. 2009. Comparison of parametric and non-parametric methods for analysing genotype environment interactions in safflower (Carthamus tinctorius L.). J. Agric. Sci. 147:601–612.CrossRefGoogle Scholar
  25. Mohammadi, R., Amri, A. 2008. Comparison of parametric and nonparametric methods for selecting stable and adapted durum wheat genotypes in variable environments. Euphytica 159:419–432.CrossRefGoogle Scholar
  26. Möhring, J., Piepho, H.P. 2009. Comparison of weighting in two-stage analysis of plant breeding trials. Crop Sci. 49:1977–1988.CrossRefGoogle Scholar
  27. Piepho, H.P. 1995. Assessing cultivar adaptability by multiple comparison with the best. Agron. J. 87:1225–1227.CrossRefGoogle Scholar
  28. Piepho, H.P. 1998. Methods for comparing the yield stability of cropping systems — A review. J. Agron. Crop Sci. 180:193–213.CrossRefGoogle Scholar
  29. Piepho, H.P., Möhring, J. 2005. Best linear unbiased prediction of cultivar effects for subdivided target regions. Crop Sci. 45:1151–1159.CrossRefGoogle Scholar
  30. Sabaghnia, N., Dehghani, H., Sabaghpour, S.H. 2006. Nonparametric methods for interpreting genotype × environment interaction of lentil genotypes. Crop Sci. 46:1100–1106.CrossRefGoogle Scholar
  31. Sabaghnia, N., Sabaghpour, S.H., Dehghani, H. 2008. The use of an AMMI model and its parameters to analyse yield stability in multi-environment trials. J. Agric. Sci. 146:571–581.CrossRefGoogle Scholar
  32. SAS Institute Inc. 2004. SAS OnlineDoc® 9.1.3 Cary, USA.Google Scholar
  33. Scapim, C.A., Pacheco, C.A., Amaral, A.T., Vieira, R.A., Pinto, R.J., Conrado, T.V. 2010. Correlations between the stability and adaptability statistics of popcorn cultivars. Euphytica 174:209–218.CrossRefGoogle Scholar
  34. Solomon, K.F., Smit, H.A., Malan, E., Du Toit, W.J. 2007. Comparison study using rank based nonparametric stability statistics of durum wheat. World J. Agric. Sci. 3:444–450.Google Scholar
  35. Shukla, G.K. 1972. Some aspects of partitioning genotype-environmental components of variability. Heredity 28:237–245.CrossRefGoogle Scholar
  36. St-Pierre, C.A., Klinck, H.R., Gauthier, F.M. 1967. Early generation selection under different environments as it influences adaptation of barley. Can. J. Plant Sci. 47:507–517.CrossRefGoogle Scholar
  37. Yan, W., Kang, M.S. 2003. GGE Biplot Analysis: A graphical tool for breeders, geneticists, and agronomists. CRC Press. Boca Raton, FL, USA.Google Scholar

Copyright information

© Akadémiai Kiadó, Budapest 2012

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.Department of Experimental Design and BioinformaticsWarsaw University of Life SciencesWarsawPoland

Personalised recommendations