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Indian Journal of Genetics and Plant Breeding (The)
Year : 2019, Volume : 79, Issue : 1
First page : ( 40) Last page : ( 47)
Print ISSN : 0019-5200. Online ISSN : 0975-6906.
Article DOI : 10.31742/IJGPB.79.1.6

Yield stability of rapeseed genotypes under drought stress conditions

Sara Mehdipour1, Abbas Rezaeizad*, Reza Azizinezhad1, Alireza Etminan2

Horticulture Crops Research Department, Kermanshah Agricultural and Natural Resources Research and Education center, AREEO, Kermanshah, Iran

1Department of Plant Breeding, Science and Research Branch, Islamic Azad University, Tehran, Iran

2Department of Biotechnology and Plant Breeding, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran

*Corresponding author's e-mail: arezaizad@yahoo.com

Online published on 2 May, 2019.

Abstract

Genotype by Environment (GxE) interactions of 29 rapeseed genotypes in normal irrigation and irrigation cut off from flowering and silique formation stages have been worked out from the data recorded during three cropping seasons. Combined variance analysis showed a significant variation for year (cropping season), moisture regimes, genotype, genotype x moisture regimes and genotype x year interactions. Results of AMMI model analysis showed that three first genotype x environment principal components (PC) were significant at 1% level of probability and fourth PC at 5% level. These four components explained 35.6, 24.4, 18.4 and 14.8 per cent of the GxE sum of squares, respectively. According to AMMI2 biplot analysis, genotypes such as L155, Neptune, Elvise, Jerry, Gk-Gabriella, Sw102, GKH0224, Julius, GKH3705 and Sarigol were positioned in the center of the biplot so had the least GxE interaction and showed the most general compatibility. Based on simultaneous selection, winter type of genotypes namely, GKH2624, SW102, HW118, GKH3705, Wpn6 and L72 were identified as high yielding and stable whereas, spring genotypes namely, Zabol10, Dalgan, Jerome and Hyola 4815 were identified as low yielding with poor stability.

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Keywords

AMMI analysis, simultaneous selection, parametric statistics, canola.

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