Arpa'da Bazı Parametrik Olmayan Stabilite İstatistiği Kullanarak Genotipik Sıralamanın Tahmini

Abstract

Assessment of the yield stability of genotypes to various test environments is useful for recommending them for farmers and should be a requirement in plant breeding programs. Sixteen barley (Hordeum vulgare L.) genotypes were tested at four research locations for three years. The trials involved a randomized complete block design with four replications in which seven nonparametric stability statistics were used to analyze yield stability. The combined analysis of variance indicated the significance of the main effects of environments and genotypes as well as genotype by environment interaction. The overall mean grain yield for all the genotypes ranged from 3804.91 kg ha for G1 to 3119.27 kg ha for G13. The most stable genotypes based on the S1 and S2 nonparametric stability statistics, were G7, G10 and G11 while the most stable genotypes based on the S3, S4, S5 and S7 statistics, were G4, G5 and G10. Regarding mean yield, it could be grasped that genotype G10 (3560.91 kg ha ) was the most favorable genotype. In this study, none of the nonparametric stability statistics were positively associated with high mean yield, and instead characterized a static concept of stability. The results of factor analysis and correlation analysis of the nonparametric stability statistics and mean yield indicated that S1 and S2 would be useful for selecting for stability.

Keywords

• Adaptability, Genotype by environment interaction, Multi-environment trials, Yield stability

Estimating genotypic ranks by several nonparametric stability statistics in Barley(Hordeum vulgare L.)

Öz

Farklı çevre koşullarında bir genotipin verim stabilitesine değerlendirilmesi, hem bu genotiplerin çiftçiler için tavsiyesinde yararlıdır hem de bitki ıslahı programlarında bir gereklilik olmalıdır. On-altı adet arpa (Hordeum vulgare L.) genotipi dört araştırma lokasyonunda üç yıl test edilmiştir. Denemeler, yedi adet parametrik olmayan stabilite istatistiğini, verim stabilitesini analiz etmek için dört tekrarlamalı tesadüf blokları deneme deseninde kurulmuştur. Birleşik varyans analizi çevre, genotip ve çevre-genotip interaksiyonunun temel etkilerininin önemini belirtmiştir. Tüm genotipler için genel ortalama tane verimi 3804,91 kg/ha (G1)’dan 3119,27 kg/ha (G13)’a kadar değişmiştir. S3, S4, S5 ve S7 istatistiklerine dayalı olarak en istikrarlı genotipler, G4, G5 ve G10 iken, S1 ve S2 parametrik olmayan stabilite istatistiklerine dayalı olarak en istikrarlı genotipler, G7, G10 ve G11 olarak bulunmuştur. Ortalama verim ile ilgili olarak, genotip G10 (3560,91 kg/ha), en uygun genotip olarak belirlenmiştir. Bu çalışmada, parametrik olmayan stabilite istatistiklerinden hiçbiri yüksek ortalama verim ile olumlu ilişkili bulunmamıştır ve bunun yerine stabilitenin statik bir kavramı olarak karakterize edilmiştir. Faktör analizi ve parametrik olmayan stabilite istatistiklerinin korelasyon analizi sonuçları, S1 ve S2’nin stabilite için seçilmelerinin için yararlı olacağını göstermiştir.

Anahtar Kelimeler

• Adaptasyon, Çevre-Genotip interaksiyonu, Çoklu-Lokasyon denemeleri, Verim stabilitesi

References

1. Adugna W, Labuschange MT (2003). Parametric and nonparametric measures of phenotypic stability in linseed (Linum usitatissimum L.),. Euphytica 129: 211–218.
2. Akçura M and Kaya Y (2008). Nonparametric stability methods for interpreting G x E interaction of bread wheat genotypes (Triticum aestivum L.). Genet. Mol. Bio. 31:906– 9
3. Annicchiarico P (2002). Defining adaptation strategies and yield stability targets in breeding programmes. In M.S. Kang, ed. Quantitative genetics, genomics, and plant breeding, p. 365–383. Wallingford, UK, CABI.
4. Balalić I, Zorić M, Miklič V, Dušanić N, Terzić S, Radić V (2011). Non-parametric stability analysis of sunflower oil yield trials. Helia, 34:67–77.
5. Becker HC, and Leon J (1988). Stability analysis in plant breeding. Plant Breed.101:1–23.
6. Dehghani H, (2008). Estimating yield stability by nonparametric stability analysis in maize (Zea mays L.). Plant Breed. Seed Sci. 58, 61–77.
7. Dehghani H, A. Ebadi and Yousefi A (2006). Biplot analysis of genotype by environment interaction for barley yield in Iran. Agron. J. 98: 388–393.
8. Ebadi-Segherloo A, S.H. Sabaghpour, H. Dehghani, and M. Kamrani. (2008). Non-parametric measures of phenotypic stability in chickpea genotypes (Cicer arietinum L.). Euphytica 2:221–229.

9. Flores F, Moreno MT, and Cubero JI. (1998). A comparison of univariate and multivariate methods to analyze environments. Field Crops Res. 56:271–286.
10. Huehn M (1979). Beitrage zur erfassung der phanotypischen stabilitat. EDV Med. Biol. 10:112– 1
11. Huehn M (1990a). Non-parametric measures of phenotypic stability: Part 1. Theory. Euphytica 47:189–194.
12. Huehn M (1990b). Non-parametric measures of phenotypic stability: Part 2. Application. Euphytica 47:195–201.
13. Huehn M (1996). Non-parametric analysis of genotype × environment interactions by ranks. p. 213–228. In M.S. Kang and H.G. Gauch (ed.) Genotype by environment interaction. CRC Press, Boca Raton, FL.
14. Hussein MA, Bjornstad A and Aastveit AH (2000). SASG ×ESTAB: A SAS program for computing genotype 3 environment stability statistics. Agron. J. 92:454–459.
15. Kang MS (1988). A rank–sum method for selecting high-yielding, stable corn genotypes. Cereal Res. Commu. 16:113–115.
16. Kang MS, and Pham HN (1991). Simultaneous selection for high yielding and stable crop genotypes. Agron. J. 83:161–165.
17. Karimizadeh R, Mohammadi M, Sabaghnia N, Shefazadeh MK (2012). Using Huehn’s nonparametric stability statistics to investigate genotype × environment interaction. Not Bot Horti Agrobo, 40:293-301
18. Kaya Y, and Taner S. (2003). Estimating genotypes ranks by nonparametric stability analysis in bread wheat (Triticum aestivum L.). J. Cent. Europ. Agric. 4:47–53.
19. Knezović Z Gunjača J (2002). Nonparametric Analysis of Yield Stability of some Winter Wheat Varieties. Agriculturae Conspectus Scientificus, 67 143-148.
20. Lu HY (1995). PC-SAS program for Estimation Huehn’s nonparametric stability statistics. Agron. J.87:888–891.
21. Miranda GV (1993). Comparação de avaliação da adaptabilidade e estabilidade de comportamento de cultivares: exemplo com a cultura do feijão (Phaseolus vulgaris L.). Master's thesis, UFV, Viçosa, MG.
22. Nassar R, and Huehn M (1987). Studies on estimation of phenotypic stability: Tests of significance for non-parametric measures of phenotypic stability. Biometrics 43:45–53. Sabaghnia N, Dehghani H and Sbaghpour SH (2006). Nonparametric methods for interpreting genotype · environment interaction of lentil genotypes. Crop Sci. 46:1100–1106
23. Sabaghnia N, Sbaghpour SH, Dehghani H (2008). The use of an AMMI model and its parameters to analyse yield stability in multi-environment trials. J. Agric. Sci. (Camb.) 146:571–581.
24. SAS Institute (1996). SAS/STAT user’s guide. v. 6, 4th ed. SAS Inst., Cary, NC.
25. Scapim, C.A., V.R. Oliveira, A.L. Braccini1, C.D. Cruz, C.A.B. Andrade, M.C.G. Vidigal. 2000. Yield stability in maize (Zea mays L.) and correlations among the parameters of the Eberhart and Russell, Lin and Binns and Huehn models. Genet. Mol. Biol. 23:387– 39
26. Thennarasu K (1995). On certain non-parametric procedures for studying genotype– environment interactions and yield stability. Ph.D. thesis. P.J. School, IARI, New Delhi, India.
27. Yan W, Rajcan I (2003). Prediction of cultivar performance based on single-versus multipleyear test in soybean. Crop Science, 43:549-555.
28. Yan W, (2002). Singular-value partitioning in biplot analysis of multienvironment trial data. Agron. J. 94:990–996.
29. Yan W, Tinker NA (2005). An integrated system of biplot analysis for displaying, interpreting, and exploring genotype by-environment interactions. Crop Sci. 45:1004–1016.
30. Yue GL, Roozeboom KL, Schapaugh WT, Liang GH (1997). Evaluation of soybean cultivars using parametric and nonparametric stability estimates. Plant Breed. 116:271–275.