African Journal of
Agricultural Research

  • Abbreviation: Afr. J. Agric. Res.
  • Language: English
  • ISSN: 1991-637X
  • DOI: 10.5897/AJAR
  • Start Year: 2006
  • Published Articles: 6576

Full Length Research Paper

Additive main effects and multiplicative interaction (AMMI) analysis of GxE interactions in rice-blast pathosystem to identify stable resistant genotypes

A. K. Mukherjee1, N. K. Mohapatra2, L. K. Bose3, N. N. Jambhulkar3 and P. Nayak4*
1Cotton Research Institute, Nagpur-440010, MP, India. 2Christ College, Cuttack-753008, Orissa, India. 3Central Rice Research Institute, Cuttack-753006, Orissa, India. 4107/C, Goutam Nagar, Cuttack-753004, Orissa, India.
Email: [email protected]

  •  Accepted: 30 October 2013
  •  Published: 14 November 2013

Abstract

Genotype x environment interaction (GEI) of 42 rice genotypes tested over nine seasons was analyzed to identify stable resistance to blast disease incited by Magnaporthe oryzae. The genotypes were raised in uniform blast nursery in a randomized complete block design with three replications. The GEI was analyzed following the regression models as well as additive main effects and multiplicative interaction (AMMI) model. AMMI analysis of variance revealed that the first two interaction principal component axes (IPCA) explained 37.28 and 33.47% of the interaction effects in 14.63 and 14.02% of interaction degrees of freedom, respectively and rest of the five IPCAs were noisy. Integrating biplot display and genotypic stability statistics enabled five groupings of genotypes based on similarities in their performance across environments. The biplot generated using the environment and genotype scores for the first two IPCAs revealed the positioning of the five host genotype groups (HG) into four sectors. HG-1 constituting of 28 genotypes exhibiting low stability index (Di values), low IPCA-1 as well as IPCA-2 scores and low mean disease scores across seasons of testing, were identified as possessing stable resistance to the disease. Although, both regression and AMMI models were equally potential in partitioning of GEI, AMMI analysis and the biplot display were more informative in differentiating genotype response over environments, describing specific and non-specific resistance of genotypes, identifying most discriminating environments and thus could be useful to plant pathologists as well as breeders in supporting breeding program decisions.

Key words: Additive main effects and multiplicative interaction (AMMI) model, rice blast disease, Magnaporthe oryzae, regression model, stable resistance.