Interpreting genotype × environment interactions for durum wheat grain yields using nonparametric methods
- 213 Downloads
- 16 Citations
Abstract
The objective of this study was to compare nonparametric stability procedures and apply different nonparametric tests for genotype × environment (G × E) interactions on grain yields of 15 durum wheat genotypes selected from Iran/ICARDA joint project grown in 12 environments during 2004–2006 in Iran. Results of nonparametric tests of G × E interaction and a combined ANOVA across environments indicated the presence of both crossover and noncrossover interactions, and genotypes varied significantly for grain yield. In this study, high values of TOP (proportion of environments in which a genotype ranked in the top third) and low values of sum of ranks of mean grain yield and Shukla’s stability variance (rank-sum) were associated with high mean yield. The other nonparametric stability methods were not positively correlated with mean yield but they characterized a static concept of stability. The results of correlation analysis indicated that only TOP and rank-sum methods would be useful for simultaneous selection for high yield and stability. These two methods identified lines Mrb3/Mna-1, Syrian-4 and Mna-1/Rfm-7 as genotypes with dynamic stability and wide adaptation. According to static stability parameters, the genotypes 12A-Mar8081 and 19A-Mar8081 with lowest grain yield were selected as genotypes with the highest stability.
Keywords
Durum wheat Nonparametric methods Dynamic and static stability Crossover interactionNotes
Acknowledgements
We thank Dr. A. Beg and Dr. N. Sabaghnia for their helpful comments and providing the SAS program used for this study. We are also grateful to respected reviewers for their valuable comments and discussions on the manuscript. Also, financial support from the Agricultural Research and Education Organization (AREO) of Iran is thankfully acknowledged.
References
- Annicchiarico P (1997) Additive main effects and multiplicative interaction (AMMI) analysis of genotype location interaction in variety trials repeated over years. Theor Appl Genet 94:1072–1077CrossRefGoogle Scholar
- Baker RJ (1988) Test for Crossover genotype-environmental interactions. Can J Plant Sci 64:405–410CrossRefGoogle Scholar
- Baker RJ (1990) Crossover genotype–environmental interaction in spring wheat. In: Kang MS (ed) Genotype-by-environment interaction and plant breeding. Department Of Agronomy, Louisiana Agric Exp Stn, Baton Rouge, LA, USA, pp 42–51Google Scholar
- Baker RJ (1991) Evaluation of a test for crossover interaction. In: Pesek J, Herman M, Hartman J (eds) Biometrics in plant breeding: proceedings of 8th Meeting of EUCARPIA Section ‘Biometrics in plant breeding’. Research Institute of Agroecology and Soil Management, Hrusovany, Czechoslovakia, pp 329–338Google Scholar
- Becker HC (1981) Correlations among some statistical measures of phenotypic stability. Euphytica 30:835–840CrossRefGoogle Scholar
- Becker HC, Leon J (1988) Stability analysis in plant breeding. Plant Breed 101:1–23CrossRefGoogle Scholar
- Bortz J, Lienert, GA, Boehnke K (1990) Verteilungsfreie Methoden in der Biostatistik. Springer Verlag, BerlinGoogle Scholar
- Bredenkamp J (1974) Nonparametric prufung von wechsewirkungen. Psychol Beitr 16:398–416Google Scholar
- Crossa J (1990) Statistical analyses of multilocation trials. Adv Agron 44:55–85Google Scholar
- De Kroon J, van der Laan P (1981) Distribution-free test procedures in two-way layouts: a concept of rank-interaction. Stat Neeri 35:189–213CrossRefGoogle Scholar
- Eberhart SA, Russell WA (1966) Stability parameters for comparing varieties. Crop Sci 6:36–40CrossRefGoogle Scholar
- Erdfelder E, Bredenkamp J (1984) Kritik mehrfaktorieller Rang varianzabalysen. Psycho Beitr 26:263–282Google Scholar
- Flores F, Moreno MT, Cubero JI (1998) A comparison of univariate and multivariate methods to analyze environments. Field Crops Res 56:271–286CrossRefGoogle Scholar
- Fox PN, Skovmand B, Thompson BK, Braun HJ, Cormier R (1990) Yield and adaptation of hexaploid spring triticale. Euphytica 47:57–64CrossRefGoogle Scholar
- Hildebrand H (1980) Asymptotosch verteilungsfreie rangtests in linearen modellen. Med Inform Stak 17:344–349Google Scholar
- Huehn VM (1979) Beitrage zur erfassung der phanotypischen stabilitat. EDV Med Biol 10:112–117Google Scholar
- Huehn M (1990) Non-parametric measures of phenotypic stability: part 1. Theory, Euphytica 47:189–194Google Scholar
- Huehn M (1996) Non-parametric analysis of genotype × environment interactions by ranks. In: Kang MS, Gauch HG (eds) Genotype by environment interaction. CRC Press, Boca Raton, FL, USA, pp 213–228Google Scholar
- Huehn M, Leon J (1995) Non-parametric analysis of cultivar performance trials: experimental results and comparison of different procedures based on ranks. Agron J 87:627–632CrossRefGoogle Scholar
- Huehn M, Nassar R (1989) On tests of significance for non-parametric measures of phenotypic stability. Biometrics 45:997–1000CrossRefGoogle Scholar
- Kang MS (1988) A rank-sum method for selecting high yielding stable corn genotypes. Cereal Res Comm 16:113–115Google Scholar
- Kang MS, Gauch HG Jr (eds) (1996) Genotype-by-environment interaction. CRC Press, Boca Raton, FL, USAGoogle Scholar
- Kubinger KD (1986) A note on non-parametric tests for the interaction on two-way layouts. Biometrics J 28:67–72CrossRefGoogle Scholar
- Lin CS, Binns MR (1994) Concepts and methods for analyzing regional trial data for cultivar and location selection. Plant Breed Rev 12:271–297Google Scholar
- Lin CS, Binns MR, Lefkovitch LP (1986) Stability analysis: where do we stand? Crop Sci 26:894–900CrossRefGoogle Scholar
- Lienert GA (1973) Verteilungsfreie methods in der Biostatistik. vol 1. 2nd edn. Verlag A. Hain, Meisenheim am Glan, GermanyGoogle Scholar
- Nachit MM, Baum M, Poreciddu E, Monneveux P, Picard E (eds) (1998) SEWANA (South Europe, West Asia and North Africa) Durum Research Network. Proc SEWANA Durum Network Workshop, 20–23 March 1995. ICARDA, Aleppo, Syria.Google Scholar
- Nassar R, Huehn M (1987) Studies on estimation of phenotypic stability: tests of significance for non-parametric measures of phenotypic stability. Biometrics 43:45–53CrossRefGoogle Scholar
- Sabaghnia N, Dehghani H, Sabaghpour SH (2006) Nonparametric methods for interpreting genotype × environment interaction of Lentil genotypes. Crop Sci 46:1100–1106CrossRefGoogle Scholar
- Scapim CA, Oliveira VR, Braccinil AL, Cruz CD, Andrade CAB, Vidigal MCG (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 23:387–393CrossRefGoogle Scholar
- Shukla GK (1972) Some aspects of partitioning genotype–environmental components of variability. Heredity 28:237–245Google Scholar
- Tai GCC (1971) Genotypic stability analysis and its application to potato regional trials. Crop Sci 11:184–190CrossRefGoogle Scholar
- Tavakoli AR, Oweis T, Ferri F, Haghighati A, Belson V, Pala M, Siadat H, Ketata H (2005) Supplemental irrigation in Iran: increasing and stabilizing wheat yield in rainfed highlands. On-Farm water husbandry research report No 5. ICARDA, Aleppo, SyriaGoogle Scholar
- Thennarasu K (1995) On certain non-parametric procedures for studying genotype–environment interactions and yield stability. PhD thesis. PJ School, IARI, New Delhi, India.Google Scholar
- Truberg B, Huehn M (2000) Contribution to the analysis of genotype by environment interactions: comparison of different parametric and non-parametric tests for interactions with emphasis on crossover interactions. Agronomy Crop Sci 185:267–274CrossRefGoogle Scholar
- Westcott B (1986) Some methods, analyzing genotype–environment interaction. Heredity 56:243–253Google Scholar
- Yue GL, Roozeboom KL, Schapaugh WT, Liang GH (1997) Evaluation of soybean cultivars using parametric and non-parametric stability estimates. Plant Breed 116:271–275CrossRefGoogle Scholar