H. Zali, S.h. Sabaghpour, E. Farshadfar, P. Pezeshkpour, M. Safikhani, R. Sarparast, A. Hashem Beygi,
Volume 11, Issue 42 (winter 2008)
Abstract
Presence of genotype × environment interaction necessitates evaluation of genotypes in a wide range of environments to find desirable genotypes. This study was carried out to determine the stability and adaptability of grain yield of 17 chickpea genotypes, in RCBD with four replications at Kermanshah, Lorestan, Ilam, Gachsaran and Gorgan Research Stations during two seasons (2003-2004). The genotype × environment interaction effect analyzed using the additive main effects and multiplicative interaction (AMMI) statistical model was significant at 1% level of probability. The sum of squares of G × E interaction was partitioned by AMMI model into four significant interaction principal component axes (IPCA). The first four principal component axes (IPCA 1, 2, 3 and 4) cumulatively contributed to 94% of total genotype by environment interaction. A biplot generated using genotypic and environmental scores of the first two AMMI components also showed that genotypes FLIP 97- 79, X95TH1 and FLIP 97- 114 were selected as stable genotypes, among which the genotype FLIP 97- 114 was outstanding for high yield stability.
R. Karimizadeh, M. Safikhani Nasimi, M. Mohammadi, F. Seyyedi, A.a. Mahmoodi, B. Rostami,
Volume 12, Issue 43 (spring 2008)
Abstract
One of the applications of Non-Parametric methods is determination of genotypes rank in different environments, which is also used as a measuring stability. A stable genotype shows similar ranks across different environments and has minimum rank variance in different environments. Non-Parametric Stability Statistics require no statistical assumptions about the distribution of the phenotypic values and are easy to use. This study was carried out to determine the ranks of 10 Lentil genotypes (Lens culinaris Medikus) across ten environments in 2002-2004, using a randomized complete block design with four replications. Analysis of Thennarasu non-parametric statistics showed that genotypes 8 and 9 had high stability by NP(1) statistic and genotypes 9, 8 and 1 had stable yield in NP(2) method. Result of the NP(3) statistic was similar to NP(1) statistic. NP(4) statistic selected genotypes 9 and 1 as the most stable genotypes and ultimately NP(5) statistic introduced 9 and 1 genotypes as stable genotypes in this experiment. Also analysis of Nassar and Huhn non-parametric statistics revealed that genotypes 1 and 2 were most stable and well adapted across ten environments. In addition, it was concluded that plots obtained by both mean yield (kg ha-1) vs.Si(1) and mean yield (kg ha-1) vs. Si(2) values could enhance visual efficiency of selection based on genotype × environment interaction. According to these configurations, genotypes in section 1 can be considered as stable and well adapted to all environments, having general adaptable ability. For recognition a daptability,Si(1) and Si(2) take preferred over other non-parametric statistics.
