Evaluation of grain yield stability of rainfed lentil genotypes by parametric and non-parametric methods

Document Type : Research Paper

Authors

1 Assistant Professor, Dryland Agricultural Research Institute, Kohgiloyeh and Boyerahmad Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Gachsaran, Iran

2 Assistant Professor, Lorestan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Khorram Abad, Iran

3 Assistant Professor, Ilam Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Ilam, Iran

Abstract

Introduction
Lentil as a legume crop is one of the most important food crops in developing countries (Karimizadeh & Mohammadi, 2010). In most cases, the interaction between environment and genotype occurs, complicating selection for improved yield among genotypes (Sabaghpour et al., 2004). A cultivar or genotype is considered to be more adaptive or stable if it has a high mean yield but a low degree of fluctuation in yielding ability when grown in diverse environments (Finlay & Wilkinson, 1963). The response of different genotypes in different environments and thus the evaluation of genotype interaction in the environment are of particular importance to researchers in plant genetics and breeding, which can help plant breeders to evaluate genotypes more accurately and select the best one. The purpose of this study was to identify and introduce superior genotypes in terms of yield and yield stability among the lines obtained from preliminary yield test.
Materials and Methods
Sixteen advanced lentil genotypes along with the control genotypes i.e. Kimia and Gachsaran selected from the advanced yield trial of the 2012-13 cropping year were used as planting material in Gachsaran, Khorramabad and Ilam areas for three years (2013-2016) in a randomized complete block design with three replications. Analysis of variance was performed separately in each environment and then Bartlett test was used to evaluate the homogeneity of experimental errors. Then the combined analysis of variance was performed on seed yield. Stability analysis were performed using environmental variance (S2i), coefficient of variation (CVi), Shukla's variance (2i), Wrick equivalence (Wi), Plaisted statistic ( ), Plaisted and Peterson statistic ( ) and superiority index (Pi) and nonparametric methods, , , , TOP and mean of rank.
Results & Discussion
Simple analysis of variance showed genetic differences among the genotypes. The combined analysis of variance was performed after Bartlett test, which confirmed variance homogeneity of experimental errors (χ2 = 9.5; P = 0.33). The combined analysis of variance indicated the significant effects of genotype, year, location and interactions of year × location, genotype × location and genotype × year × location. The mean seed yield of genotypes showed that out of 18 studied genotypes, seven genotypes produced higher yields than the average yield of genotypes in the all environments (1566.39 kg.ha-1), so that the highest seed yield were seen in the genotypes 15 and 16, followed by genotypes 8, 12, 11, 5 and 2. Based on environmental variance (S2i), the genotypes 3, 7, 6 and 13 and based on the coefficient of environmental variation (CVi), the genotypes 3, 7, 6 and 15 were identified as stable genotypes. Plaisted and Plaisted and Peterson methods identified the genotypes 4, 9, 2, 10 and 3 as stable genotypes. Wrick equivalence and Shukla variance also introduced the genotypes 4, 9, 2, 10, 3 and 12 as stable genotypes. The Lin and Binns superiority index identified the genotypes 16, 8, 15, 12 and 11 as the most stable genotypes. The genotypes 4, 2, 3, 10 and 9 were the most stable genotypes based on nonparametric index. Based on the index, the genotypes 1, 2, 3, 4, 6, 7, 9, 10 and 18 and based on statistics, the genotypes 3, 4, 10, 9, 1, 7, 2 and 6 were stable genotypes. Based on Fox nonparametric index, the genotypes 16, 11, 2, 8, 12, 13 and 14 were stable genotypes. The genotypes 12, 2, 9, 16, 11, 8, 4 and 3 were more stable based on the total Kong rank. The principal component analysis to evaluate the relationship between seed yield and stability indices showed that seed yield had the highest correlation with MID, TOP and PI. Therefore, these three indices can be used as the best indices to identify superior genotypes in terms of seed yield and stability.
Conclusion
In general, the genotypes 2, 5, 8, 11, 12, 13, 15 and 16 gave higher average yield or equal to Gachsaran control seed yield and were also stable. The genotype 16 and 11 were the most stable genotypes based on MID, TOP and PI and also had the highest seed yield and could be candidates to be released as new cultivars.

Keywords


Azizi Chakherchaman, S.H., Mostafaei, H., Hassanpanah, D., Yarnia, M., and Zeinalzadeh Tabrizi, H. 2008. Evaluation of grain yield stability of lentil cultivars under Ardabil rainfed conditions. Journal of Agricultural Sciences, 2(6): 1-10 (In Persian with English Summary).
Bagheri, A., Goldani, M., and HassanZadeh, M. 1997. Agronomy and breeding lentils, translated Universial Jihad Publication of Mashhad, p. 248. (In Persian).
Biçer, B.T., Kizilgeci, F., Albayrak, O., Akinci, C., and Yildirim, M. 2018. Stability Parameters in Lentil Genotypes. El-Cezerî Journal of Science and Engineering, 5(2): 287-291. 
Biçer, T., and Şarkar, D. 2006. Stability Parameters in Lentil. Journal of Central European Agriculture, 7(3): 439-444.
Eberhart, S. A., and Russel, W. A. 1966. Stability parameters for comparing varieties, Crop Science, 6: 36-40.
Finlay, K.W., and Wilkinson, G. M. 1963. The analysis of adaptaion in the plant breeding programs. Australian Journal of Agricultural Research, 14: 742-745.
Fox, P.N., Skovmand, B., Thompson, B.K., and Braun, H.J. 1990. Yield and adaptation of hexaploid spring triticale. Euphytica, 47(1): 57–64.
Francis, T.R., and Kannenberg, L.W. 1978. Yield stability studies in short-season maize. 1. A descriptive method for grouping genotypes. Canadian Journal of Plant Science, 58: 1029-1034.
Gauch, H.G., and Zobel, R.W. 1997. Identifying mega-environments and targeting genotypes. Crop Science, 37(1): 311–326. doi:10.2135/cropsci199 7.0011183X003700020002x.
Huhn, M. 1979. Beitrage zur Erfassung der phanotypischen stabilitat. I. Vorschlag einiger auf Ranginformationnen beruhenden stabilitat sparameter. EDV in Medizin und Biologie, 10: 112–117. (in German with English Summary).
Huhn, M., and Leon, J. 1995. Nonparametric analysis of cultivar performance trials: experimental results and comparison of different procedures based on ranks. Agronomy Journal, 87:627-632.
Huhn, M., and Nassar, R. 1989. On tests of significance for nonparametric measures of phenotypic stability. Biometrics, 45: 997-1000.
Kang, M. S. 1988. A rank-sum method for selecting high yielding stable corn genotypes. Cereal Research Communications, 16: 113–115.
Kang, M.S., and Pham, H.N. 1991. Simultaneous selection for high yielding and stable crop genotypes. Agronomy Journal, 83: 161–165.
Karimizadeh, R., and Mohammadi., M. 2010. AMMI adjustment for rainfed lentil yield trials in IRAN. Bulgarian Journal of Agricultural Science, 16 (1): 66-73.
Karimizadeh, R., and Mohammadi., M. 2011. Determining genotype × environment interaction by parametric and nonparametric methods of phenotypic stability in lentil genotypes. Modern Genetics Journal, 6 (1): 75-86 (In Persian with English Summary).
Karimizadeh, R., Safikhani, M., Mohammadi, M., Seyyedi, F., Mahmoodi, A., and Rostami, B. 2008. Determining rank and stability of lentil in rainfed condition by nonparametric statistics. Journal of Science and Technology in Agriculture and Natural Resources, 43 (1): 93-103 (In Persian with English Summary).
Karimizadeh, R., Faraidi, Y., Mahmoudi, A.A., Mohammadi, M., and Sadeghzadeh Ahri, D. 2014. Evaluation of genotype × environment interaction by AMMI method in lentil lines. The 5th Iranian Pulse Crops Conference, Karaj, Iran, 26 February (In Persian with English Summary).
Lin, C.S., and Butler, G. 1990. Cluster analysis for analyzing two way classification data. Agronomy Journal, 82: 344-348.
Lin, C.S., and Binns, M. R. 1988. Amethod of analysing cultivar x location x year expriments a new stability parameter. Theoretical and Applied Genetics, 76: 425-430.
Mendiburu, F. 2019. Agricolae tutorial. http://tarwi.lamolina.edu.pe/~fmendiburu.
Nassar, R., and Huhn, M. 1987. Studies on estimation of phenotypic stability: Test of significance for non- parametric measures of phenotypic stability. Biometrics, 43: 45–53.
Perkins, J.N., and Jinks, J.C. 1968. Environmental and genotype x environmental components of variability. IV. Non-linear intraction for multiple inbred lines. Heredity, 23: 525-535.
Plaisted, R.L. 1960. A shorter method for evaluating the ability of selections to yield consistently over locations. American Potato Journal, 37: 166–172.
Plaisted, R.L., and Peterson, L.C. 1959. A technique for evaluating the ability of  selections to yield consistently in different locations or seasons. American Potato Journal, 36: 381–385.
Roemer, T. 1917. Sin die ertragsreichen sorten ertragssicherer. DLG-Mitteilungen 32: 87-89.
Sabaghnia, N., Dehghani, H., and Sabaghpour, S. H. 2006. Nonparametric methods for interpreting genotype × environment interaction of lentil genotypes. Crop Science, 46(3): 1100–1106.
Sabaghnia, N., Dehghani, H., and Sabaghpour, S. H. 2008. Graphic analysis of genotype by environment interaction for lentil yield in Iran. Agronomy Journal, 100: 760-764.
Sabaghpour, S.H., Safikhni, M., Sarker, A., Ghaffri, A., and Ketata, H. 2004. Present status and future prospects of lentil cultivation in Iran. Inpruceeding of 5th European Coferance on Grain Legums, 7 to 11 June 2004, Dijon, France.
Shukla, G.H. 1972. Some statistical aspects of partitioning genotype- environment components of variability. Heredity. 29: 237-245.
Wricke, G. 1962. Uber eine methode zur refassung der okologischen streubretite in feldversuchen. Flazenzuecht, 47: 92-96.
Yan, W., Hunt, L.A., Sheny, Q., and Szlavnics, Z. 2000. Cultivar evaluation and mega-environment investigation based on the GGE biplot. Crop Science, 40: 597- 605.