Applicability of joint regression and biplot models for stability analysis in multi-environment barley (Hordeum vulgare) trials


Abstract views: 128 / PDF downloads: 53

Authors

  • VISHNU KUMAR ICAR-Indian Institute of Wheat and Barley Research, Karnal, Haryana 132 001
  • A S KHARUB ICAR-Indian Institute of Wheat and Barley Research, Karnal, Haryana 132 001
  • R P S VERMA ICARDA, Rabat, Morocco
  • AJAY VERMA ICAR-Indian Institute of Wheat and Barley Research, Karnal

https://doi.org/10.56093/ijas.v86i11.62923

Keywords:

AMMI and GGE biplots, GEI, Joint regression method, Stability

Abstract

GGE and AMMI biplot methods with Eberhart and Russell regression model were applied on the set of 18 barley (Hordeum vulgare L.) genotypes grown in 6 environments for quick and relevant method vis-a-vis to delineate genotype by environment interaction, stable genotypes and environmental discrimination. The average grain yield over the locations was depicted as 41.97 q/ha, which ranged from 31.82 (Karnal) to 55.52 q/ha (Bhatinda). The genotype DWRB 91 (47.51 q/ha) exhibited the highest grain yield followed by DWRB 121 (46.35 q/ha), DWRB 123 (46.04 q/ha) and DWRB 128 (44.70 q/ha) over the locations. In Eberhart and Russell model, the genotypes DWRB 124 and PL 880 were found suitable for favourable environments and DWRB 128 for poor environments. In AMMI analysis, IPCA 1 and IPCA 2 altogether captured 74.73% of the interaction mean squares, while in GGE biplot, PC 1 and PC 2 captured 36.51% and 26.44% interaction variation,respectively. The genotypes BH 992, DWRB 121, DWRB 123, RD 2897 and checks BH 902 and DWRB 91 were high yielding and as well as found stable in GGE and AMMI 1 biplot. The test environments Durgapura and Modipuram exhibited different niches, whereas, Hisar, Ludhiana, Bhatinda and Karnal were representative with better discriminating ability. Between biplot models applied, the GGE biplots were clear in visualization for polygon view, genotypic stability and environmental discrimination. The GGE method considered both G+GE for biplot generation and found most suitable for stability analysis.

Downloads

Download data is not yet available.

References

Alwala S, Kwolek T, McPherson M, Pellow J and Meyer D. 2010.A comprehensive comparison between Eberhart and Russell joint regression and GGE biplot analyses to identify stable and high yielding maize hybrids. Field Crop Research 119: 225–30.

Asfaw A, Alemayehu F, Gurum F and Atnaf M. 2009. AMMI and SREG GGE biplot analysis for matching varieties onto soybean production environments in Ethiopia. Scientific Research and Essay 4: 1 322–30.

Dehghani H, Ebadi A and Yousefi A. 2006.Biplot analysis of genotype by environment interaction for barley yield in Iran.Agronomy Journal 98: 388–93.

Eberhart S A and Russell W A. 1966. Stability parameters for comparing varieties.Crop Science 6: 36–40.

FAOSTAT. 2016. FAOSTAT. Food and Agriculture Organization (FAO) of the United Nations, Rome, Italy. Available at http://faostat3.fao.org (accessed Jan. 2016).

Finlay K W and Wilkinson G N. 1963.The analysis of adaptation in a plant breeding programme.Crop and Pasture Science 14: 742–54.

Flores F, Moreno M T and Cubero J I. 1998. A comparison of univariate and multivariate methods to analyze G×E interaction. Field Crops Research 56: 271–86.

Gabriel K R. 1971. The biplot graphic display of matrices with application to principal component analysis. Biometrika 58: 453–67.

Gauch H G. 1988. Model selection and validation for yield trials with interaction. Biometrics 88: 705–15.

Gauch H G. 2006. Statistical analysis of yield trials by AMMI and GGE.Crop Science 46: 1 488–500.

Gauch H G, Piepho H P andAnnicchiarico P. 2008. Statistical analysis of yield trials by AMMI and GGE: Further considerations. Crop Science 48: 866–89.

Kuchanur P H, Salimath P M, Wali M Cand Hiremath C. 2015. GGE biplot analysis for grain yield of single cross maize hybrids under stress and non-stress conditions. Indian Journal of Genetics and Plant Breeding 75: 514–17.

Kumar V, Khippal A, Singh J, Selvakumar R, Malik R, Kumar D, Kharub A S, Verma R P S and Sharma I. 2014. Barley research in India: retrospect and prospects. Journal of Wheat Research 6(1): 1–20.

Kumar V, KumarR, VermaRPS, VermaA and SharmaI. 2013. Recent trends in breeder seed production of barley (H. vulgare L.) in India. Indian Journal of Agricultural Sciences 83: 576–8.

Lin C S and Binns M R. 1988.A superiority measure of cultivar performance for cultivar location data.Canadian Journal of Plant Science 68: 193–8.

Rad M R N, Kadir M A, Rafii M Y, Jaafar H Z, Naghavi M R and Ahmadi F. 2013. Genotype× environment interaction by AMMI and GGE biplot analysis in three consecutive generations of wheat (Triticum aestivum) under normal and drought stress conditions. Australian Journal of Crop Science 7: 956–61.

Shukla G K. 1972. Some statistical aspects of partitioning genotype–environmental components of variability. Heredity 29: 237–45.

Silva R R, Riede C R, Fonseca I C D B, Zucareli C and Benin G. 2016. Investigating suitable test locations and mega- environments for evaluating spring wheat in Brazil. Australian Journal of Crop Science 10: 137–43.

Sousa L B, Hamawaki O T, Nogueira A P, Batista R O, Oliveira V M and Hamawaki R L. 2015. Evaluation of soybean lines and environmental stratification using the AMMI, GGE biplot, and factor analysis methods.Genetics and Molecular Research 14(4): 12 660–74.

Villegas V H, Wright E M and Kelly J D. 2016. GGE biplot analysis of yield associations with root traits in a Mesoamerican bean diversity panel. Crop Science 56: 1081–94.

Yan W, Hunt L A, Sheng Q andSulavnics Z. 2000.Cultivar evaluation and mega-environment investigation based on the GGE biplot. Crop Science 40: 597–605.

Yan W, Kang M S, Ma B, Woods S and Cornelius P L. 2007. GGE biplot vs. AMMI analysis of genotype-by-environment data. Crop Science 47: 643-53.

Yan W and Tinker N A. 2006. Biplot analysis of multi- environment trial data: principles and applications. Canadian Journal of Plant Science 86: 623–45.

Downloads

Submitted

2016-11-09

Published

2016-11-09

How to Cite

KUMAR, V., KHARUB, A. S., VERMA, R. P. S., & VERMA, A. (2016). Applicability of joint regression and biplot models for stability analysis in multi-environment barley (Hordeum vulgare) trials. The Indian Journal of Agricultural Sciences, 86(11), 1443–8. https://doi.org/10.56093/ijas.v86i11.62923

Issue

Section

Articles
Citation