Comparison of Ammi, Parametric and Non-Parametric Models in Identifying High-Yielding and Stable Oilseed Rape Genotypes

Abstract

One of the complex issue in the way of releasing new high-yielding and stable oilseed rape ‎cultivars is genotype by environment interaction (GEI) which reduce selection efficiency. In ‎the current study, parametric and non-parametric statistics as well as the AMMI model have ‎been compared to identify the best stability models to clarify GEI complexity. The ‎experiment has been conducted in the warm regions of Iran including; Gorgan, Sari, Zabol, ‎and Hajiabad during two cropping seasons (2016-2017 and 2017-2018) for 16 genotypes in a ‎randomized complete block design with three replications. The AMMI analysis of variance on ‎grain yield showed the significant effects of genotype, environment, and the interaction ‎effects of GEI on yield. Based on the AMMI ANOVA, the major contribution of GEI was ‎captured by the first and second interaction principal component axes (IPCA1 and IPCA2) ‎which explained 34.29% and 29.81% of GEI sum of the square, respectively. Additionally, ‎Different parametric and non-parametric stability methods including; bi, S2di, CVi, W2i, σ2i, Pi, ‎Si(1), Si(2), Si(3), Si(6), Npi(1), Npi(2), Npi(3), Npi(4), KR and TOP have also investigated. Based on ‎AMMI, parametric, and non-parametric stability statistics, genotypes G2 (SRL-95-7) and G9 ‎‎(SRL-95-16)‎‏ ‏were selected as the stable and high-yielding genotypes. Likewise, Principal ‎component analysis based on rank correlation matrix enabled us to distinguish high-yielding ‎genotypes to stable (high-yielding genotypes in various environments) and unstable (high-‎yielding genotypes in low-yielding environments) ones. Furthermore, a significant Spearman ‎correlation was observed between yield mean and GSI, Pi, Si(3), Si(6), Npi(3), Npi(4), and KR. ‎Therefore, different efficient strategies were identified in this study‏ ‏and since we looked up ‎high-yielding and stable genotypes, G2 (SRL-95-7) and G9 (SRL-95-16)‎‏ ‏were finally ‎selected.‎

Keywords

• AMMI
• Brassica napus
• Genotype by environment interaction (GEI)
• Parametric and non-‎parametric stability statistics
• Principal component analysis

Supporting Institution

Seed and Plant Improvement Institute, Agricultural Research, Education and Extension ‎Organization (AREEO‎)

Project Number

‎0-03-03-327-961673‎

Thanks

We would like to thank the Seed and Plant Improvement Institute of Iran for supporting us in ‎conducting the research with grant number 0-03-03-327-961673.‎

References

1. Agahi K., J. Ahmadi, H.A. Oghan, M.H. Fotokian, S.F. Orang. 2020. Analysis of genotype× ‎environment interaction for seed yield in spring oilseed rape using the AMMI model. ‎Crop Breed. Appl. Biotechnol. 20‎
2. Becker H., J. Leon. 1988. Stability analysis in plant breeding. Plant breed. 101:1-23‎
3. Bocianowski J., A. Liersch, K. Nowosad. 2020. Genotype by environment interaction for ‎alkenyl glucosinolates content in winter oilseed rape (Brassica napus L.) using additive ‎main effects and multiplicative interaction model. Curr. Plant Biol. 100137‎
4. Brandiej E., B. Meverty. 1994. Genotype× environmental interaction and stability of seed yield ‎of oil rapeseed. Crop Sci. 18:344-353‎
5. Carbonell S.A.M., J.A.d. Azevedo Filho, L.A.d.S. Dias, A.A.F. Garcia, L.K.d. Morais. 2004. ‎Common bean cultivars and lines interactions with environments. Scientia Agricola. ‎‎61:169-177‎
6. Eberhart S.t., W. Russell. 1966. Stability parameters for comparing varieties 1. Crop science ‎‎6:36-40‎
7. FAO. 2018. Food and Agriculture Organization of the United Nations, Food and Agricultural ‎Commodities Production. Available ongenotype: http://www.fao.org/statistics/en . ‎
8. Fox P., B. Skovmand, B. Thompson, H.-J. Braun, R. Cormier. 1990. Yield and adaptation of ‎hexaploid spring triticale. Euphytica 47:57-64‎

9. Francis T., L. Kannenberg. 1978. Yield stability studies in short-season maize. I. A descriptive ‎method for grouping genotypes. Can. J. Plant Sci. 58:1029-1034‎
10. Gauch Jr H.G. 2006. Statistical analysis of yield trials by AMMI and GGE. Crop science ‎‎46:1488-1500‎
11. Getahun A. 2017. Adaptability and stability analysis of groundnut genotypes using AMMI ‎model and GGE-biplot. J. crop sci. biotech. 20:343-349‎
12. Huehn M. 1990. Nonparametric measures of phenotypic stability. Part 1: Theory. Euphytica ‎‎47:189-194‎
13. Huehn M. 1990. Nonparametric measures of phenotypic stability. Part 2: Applications. ‎Euphytica 47:195-201‎
14. Hühn M., R. Nassar. 1989. On tests of significance for nonparametric measures of phenotypic ‎stability. Biometrics:997-1000‎
15. Kang M. 2004. Breeding: genotype by environment interaction. In ‘Encyclopedia of plant and ‎crop science’.(Ed. RM Goodman) pp. 218–221Marcel Dekker: New York.‎
16. Kempton R. 1984. The use of biplots in interpreting variety by environment interactions. J. ‎Agri. Sci. 103:123-135‎
17. Lin C.-S., M.R. Binns. 1988. A superiority measure of cultivar performance for cultivar× ‎location data. Can. J. Plant Sci. 68:193-198‎
18. Lin C.-S., M.R. Binns, L.P. Lefkovitch. 1986. Stability Analysis: Where Do We Stand? 1. Crop ‎Sci. 26:894-900‎
19. Lobell D.B., S.M. Gourdji. 2012. The influence of climate change on global crop productivity. ‎Plant Physiol. 160:1686-1697‎
20. Marjanović-Jeromela A., N. Nagl, J. Gvozdanović-Varga, N. Hristov, A. Kondić-Špika, M.V.R. ‎Marinković. 2011. Genotype by environment interaction for seed yield per plant in ‎rapeseed using AMMI model. Pesquisa Agropecuária Brasileira 46:174-181‎
21. Nowosad K., A. Liersch, W. Poplawska, J. Bocianowski. 2017. Genotype by environment ‎interaction for oil content in winter oilseed rape (Brassica napus L.) using additive main ‎effects and multiplicative interaction model. Indian J. Genet. Plant Breed. 77:293-297‎
22. Oghan H.A., N. Sabaghnia, V. Rameeh, H.R. Fanaee, E. Hezarjeribi. 2016. Univariate stability ‎analysis of genotype× environment interaction of oilseed rape seed yield. Acta ‎Universitatis Agriculturae et Silviculturae Mendelianae Brunensis. 64:1625-1634‎
23. Pacheco A., M. Vargas, G. Alvarado, F. Rodríguez, M. López, J. Crossa, J. Burgueño. 2016. ‎GEA-R (Genotype× Environment Analysis whit R for Windows.) Version 4.0 ‎International Maize and Wheat Improvement Center.‎
24. Pinthus M.J. 1973. Estimate of genotypic value: A proposed method. Euphytica 22:121-123‎
25. Pourdad S., M. Moghaddam, A. Faraji, H. Naraki. 2014. Study on different non-parametric ‎stability methods on seed yield of spring rapeseed varieties and hybrids. Iranian J. Field ‎Crop Sci. 44‎
26. Purchase J., H. Hatting, C. Van Deventer. 2000. Genotype× environment interaction of winter ‎wheat (Triticum aestivum L.) in South Africa: II. Stability analysis of yield performance. ‎South African J. Plant Soil. 17:101-107. DOI ‎https://doi.org/10.1080/02571862.2000.10634877‎
27. Resketo P., L. Szabo. 1992. The effect of drought on development and yield components of ‎soybean. Int. J. Trop. Agric. 8:347-354‎
28. Richards R. 1978. Genetic analysis of drought stress response in rapeseed (Brassica campestris ‎and B. napus). I. Assessment of environments for maximum selection response in grain ‎yield. Euphytica. 27:609-615‎
29. Roy D. 2000 Plant breeding: Analysis and exploitation of variation. Alpha Science Int'l Ltd.‎
30. Sabaghnia N., S. Sabaghpour, H. Dehghani. 2008. The use of an AMMI model and its ‎parameters to analyse yield stability in multi-environment trials. J. Agri. Sci. 146:571‎
31. Shukla G. 1972. Some statistical aspects of partitioning genotype environmental components of ‎variability. Heredity. 29:237-245‎
32. Soughi H., N.B. Jelodar, G. Ranjbar, M. Pahlevani. 2016. Simultaneous selection based on yield ‎and yield stability in bread wheat genotypes. J. Crop Breed. 8:119-125‎
33. Tabari H., H. Abghari, P. Hosseinzadeh Talaee. 2012. Temporal trends and spatial ‎characteristics of drought and rainfall in arid and semiarid regions of Iran. Hydrol. ‎Process. 26:3351-3361‎
34. Tai G.C. 1971. Genotypic stability analysis and its application to potato regional trials. Crop ‎science 11:184-190‎
35. Tiiennarasu K. 1995. On Certain Non-Parametric Procedures For Studying Genotype-‎Environmentinteractions. And Yield StabilityIARI, Division of Agricultural Statistics: ‎New Delhi.‎
36. Torbaghan M.E., A. Mirzaee, M. Jamshid Moghaddam, M. Eskandari Torbaghan, A. Mirzaee. ‎‎2014. Analysis of genotype× environment interaction for seed yield in spineless ‎safflower (Carthamus tinctorius L.) genotypes. Crop Breed. J. 4:47-56‎
37. Warwick S., A. Francis, I. Al-Shehbaz. 2006. Brassicaceae: species checklist and database on ‎CD-Rom. Plant Syst. Evol. 259:249-258‎
38. Wricke G. 1962. Uber eine Methode zur Erfassung der okologischen Streubreite in ‎Feldverzuchen. Z pflanzenzuchtg 47:92-96‎
39. Wu W., B.L. Ma, J.K. Whalen 2018 Enhancing rapeseed tolerance to heat and drought stresses ‎in a changing climate: perspectives for stress adaptation from root system ‎architectureAdvances in Agronomy. Elsevier, pp. 87-157.‎
40. Yan W., M.S. Kang 2002 GGE biplot analysis: A graphical tool for breeders, geneticists, and ‎agronomists. CRC press
41. Yan W., M.S. Kang, B. Ma, S. Woods, P.L. Cornelius. 2007. GGE biplot vs. AMMI analysis of ‎genotype‐by‐environment data. Crop Sci. 47:643-653‎
42. Zali H., O. Sofalian, T. Hasanloo, A. Asghari. 2016. AMMI and GGE Biplot Analysis of Yield ‎Stability and Drought Tolerance in Brassica napus L. Agri. Commun. 4:1-8‎
43. Zobel R.W., M.J. Wright, H.G. Gauch Jr. 1988. Statistical analysis of a yield trial. Agron. J. ‎‎80:388-393‎