Non-parametric Measures for Yield Stability in Grass Pea (Lathyrus sativus L.) Advanced Lines in Semi Warm Regions

Authors
1 Department of Plant Breeding, Faculty of Engineering and Technology, Imam Khomeini International University, P. O. Box. 34149-16818, Qazvin, Islamic Republic of Iran.
2 Gachsaran Agricultural Research Station, Gachsaran, Islamic Republic of Iran.
3 Kermanshah Dry-land Agricultural Research Institute, Kermanshah, Islamic Republic of Iran.
4 Lorestan Dry-land Agricultural Research Institute, Lorestan, Islamic Republic of Iran.
Abstract
Multi-environment trials play a significant role in selecting the best cultivars to be used at different locations. The objective of this study was to identify grain and forage yields stability of grass pea advanced lines across different locations. The 14 advanced lines of grass pea, developed by the International Center for Agricultural Research in Dry Areas (ICARDA), were tested at three different research stations in semi-warm regions of Iran for three consecutive years. Ten non-parametric measures of stability were used to identify stable lines across nine environments. Three non-parametric tests (Bredenkamp, Hildebrand and De Kroon and Van der Laan) for Genotype-Environment (GE) interaction were highly significant, recommending differential responses of the lines to the test environments. Mean yields had a significant positive correlation with Si(6), NP2, NP3, NP4, Fox-rank and Kang’s rank-sum statistics. The results of correlation analysis and principal components analysis indicated that only non-parametric superiority measure could be useful for simultaneous selection of high yielding and stable lines. According to cluster analysis by forage and grain mean yields and non-parametric statistics, the line L3 with the highest forage and grain yields and Fox-rank as well as the lowest values of other non-parametric statistics could be introduced as high yielding stable cultivar for rain-fed conditions of semi-warm areas.

Keywords


1. Adugna, W. and Labuschagne, M. 2003. Parametric and Nonparametric Measures of Phenotypic Stability in Linseed (Linum Usitatissimum L.). Euphytica, 129: 211-218.
2. Ahmadi, A., Mohammadi, A. and Najafi Mirak, T. 2012a. Targeting Promising Bread Wheat (Triticum aestivum L.) Lines for Cold Climate Growing Environments Using AMMI and SREG GGE Biplot Analyses. J. Agr. Sci. Tech., 14: 645-657
3. Ahmadi, J., Vaezi, B., Shaabani, A. and Khademi, K. 2012b. Multi-environment Yield Trials of Grass Pea (Lathyrus sativus L.) in Iran Using AMMI and SREG GGE. J. Agr. Sci. Tech., 14: 1075-1085.
4. Akcura, M. and Kaya, Y. 2008. Nonparametric Stability Methods for Interpreting Genotype by Environment Interaction of Bread Wheat Genotypes (Triticum aestivum L.). Genet. Mol. Biol., 31: 906-913.
5. Bhargava, A., Shukla, S. and Ohri, D. 2007. Evaluation of Forage Yield and Leaf Quality Traits in Chenopodium Spp. in Multiyear Trials. Euphytica, 153: 199-213.
6. Bredenkamp, J. 1974. Nonparametric Prufung von Wechsewirkungen. Psychol. Beitr., 16: 398-416.
7. De Kroon, J. and Van der Laan, P. 1981. Distribution-free Test Procedures in Two-way Layouts: A Concept of Rank Interaction. Stat Neeri., 35: 189-213.
8. Eberhart, S. and Russell, W. 1966. Stability Parameters for Comparing Varieties. Crop. Sci., 6: 36-40.
9. Flores, F., Moreno, M. T. and Cubero, J. I. 1998. A Comparison of Univariate and Multivariate Methods to Analyze Environments. Field. Crop. Res., 56: 271-286.
10. Fox, P., Skovmand, B., Thompson, B., Braun, H. J. and Cormier, R. 1990. Yield and Adaptation of Hexaploid Spring Triticale. Euphytica, 47: 57-64.
11. Hildebrand, H. 1980. Asymptotosch Verteilungsfreie Rangtests in Linearen Modellen. Med. Inform. Stak., 17: 344-349.
12. Huehn, M. 1979. Beitrage Zur Erfassung der Phanotypischen Stabilitat. Edv. Med. Biol., 10: 112-117.
13. Huehn, M. 1990. Nonparametric Measures of Phenotypic Stability. Part 1. Theory. Euphytica, 47: 189-194.
14. Huehn, M. 1996. Non-parametric Analysis of Genotype×Environment Interactions by Ranks. In: “Genotype by Environment Interaction”, (Eds.): Kang, M. S. and Gauch H. G. CRC Press, Boca Raton, FL, USA, PP. 213-228
15. Huhn, M. and Leon, J. 1995. Nonparametric Analysis of Cultivar Performance Trials: Experimental Results and Comparison of Different Procedures Based on Ranks. Agron. J., 87: 627-632.
16. Jamshidmoghaddam, M. and Pourdad, S.S. 2013. Genotype×Environment Interactions for Seed Yield in Rainfed Winter Safflower (Carthamus tinctorius L.) Multi-Environment Trials in Iran. Euphytica, 190: 357-369.
17. Kang, M. S. 1990. Genotype-by-Environment Interaction and Plant Breeding. Louisana State University Agricultural Center, Baton Rouge, LA, USA.
18. Kang, M. 1988. A Rank-Sum Method for Selecting High-Yielding, Stable Corn Genotypes. Cereal. Res. Commun., 16: 113-115.
19. Kang, M., Gorman, D. and Pham, H. 1991. Application of a Stability Statistic to International Maize Yield Trials. Theor. Appl. Genet., 81: 162-165.
20. Kasprzak, M. and Rzedzicki, Z. 2008. Application of Everlasting Pea Wholemeal in Extrusion-cooking Technology. Int. Agrophys., 22: 339-347.
21. Lin, C. S., Binns, M. R. and Lefkovitch, L. P. 1986. Stability Analysis: Where Do We Stand? Crop. Sci., 26: 894-900.
22. Milczak, M., Pedzinski, M., Mnichowska, H., Szwed-Urbas, K. and Rybinski, W. 2001. Creative Breeding of Grasspea (Lathyrus sativus L.) in Poland. Lathyrus Lathyrism Newsletter, 2: 85-88.
23. Mohammadi, R., Abdulahi, A., Haghparast, R. and Armion, M. 2007. Interpreting Genotype×Environment Interactions for Durum Wheat Grain Yields using Nonparametric Methods. Euphytica, 157: 239-251.
24. Mohammadi, R. and Amri, A. 2008. Comparison of Parametric and Non-Parametric Methods for Selecting Stable and Adapted Durum Wheat Genotypes in Variable Environments. Euphytica, 159: 419-432.
25. Nassar, R. and Huehn, M. 1987. Studies on Estimation of Phenotypic Stability: Tests of Significance for Nonparametric Measures of Phenotypic Stability. Biometric., 43: 43-53.
26. Sabaghnia, N., Dehghani, H. and Sabaghpour, S. H. 2006. Nonparametric Methods for Interpreting Genotype×Environment Interaction of Lentil Genotypes. Crop. Sci., 46: 1100-1106.
27. SAS Institute. 1987. SAS/STAT User’s Guide: Version. 9.1. SAS Inst Inc., Cary, NC, USA.
28. Scapim, C. A., Pacheco, C. A. P., do Amaral Júnior, A. T., Vieira, R. A., Pinto, R. J. B. and Conrado, T. V. 2010. Correlations between the Stability and Adaptability Statistics of Popcorn Cultivars. Euphytica, 174: 209-218.
29. Segherloo, A. E., Sabaghpour, S. H., Dehghani, H. and Kamrani, M. 2008. Non-parametric Measures of Phenotypic Stability in Chickpea Genotypes (Cicer arietinum L.). Euphytica, 162: 221-229.
30. Shukla, G. 1972. Some Statistical Aspects of Partitioning Genotype Environmental Components of Variability. Hered., 29: 237-245.
31. STATISTICA Statistical Software. 2007. STATISTICA Data Analysis Software System: Version 8. Sta Stof Inc., North Melbourne, Australia.
32. Tai, G. C. 1971. Genotypic Stability Analysis and Its Application to Potato Regional Trials. Crop. Sci., 11: 184-190.
33. Thennarasu, K. 1995. On Certain Non-parametric Procedures for Studying Genotype Environment Interactions and Yield Stability. PhD., PJ School IARI, New Delhi, India.
34. Ward, J. H. 1963. Hierarchical Grouping to Optimize an Objective Function. J. Am. Stat. Assoc., 58: 236-244.
35. Zobel, B. and Talbert, J. 1984. Applied Forest Tree Improvement. Wiley, New York City, New York, United States.