Showing 4 results for Stability Analysis
J. Lameie Heravani, N. Nemati, R. Bozorgipour, Z. Hosseini - Negad,
Volume 8, Issue 4 (1-2005)
Abstract
In order to find the high-yielding and adaptable cultivars in different environments, eight cotton genotypes including two early maturity hybrids were studied and compared with the Varamin cultivar as control in a randomized complete block design with four replications in six regions in 1997 and 1998. Using Barttlet test, some of the environments were eliminated from statistical analysis. Therefore, combined analysis of variance and other statistical calculations were carried out based on environment (Year×Location) on the assumption that environment was randomized and cultivars remained constant during the entire study. In combined analysis of variance, genotypic effect (in yield) was significant at 1% probability level. Combined analysis of variance also showed significant differences for the main effect of environment and genotype×environment intreaction at 1% probability level. To select the best genotype with a high and stable yield, parametric statistics of stability including type 1 stability (S2i,CVi), type II stability (bi,σi2, w2i), type III stability (Sd2i) as well as non-parametric statistics such as mean of genotypic rank (R), standard deviation of rank (SDR), and simultaneous selection of parametric and nonparametric statistics for yield and stability (Ysi) were calculated. Overall, considering the important agronomic and technological characteristics of genotypes such as yield, earliness, span length, fiber strength, percentage of uniformity, and micronariae index, the hybrid Coker×Bulgar was selected as high-yielding and stable cultivar to be substituted for Varamin cultivar across the planting area.
M. Shahmohamadi, H. Dehghani, A. Yousefi,
Volume 9, Issue 1 (4-2005)
Abstract
To determine yield stability and to evaluate genotype interaction with environment interaction, 18 genotype of barley (Hordeum vulgare L.) and a control group were evaluated in a randomized complete block design with 4 replications in 3 successive years (1997-2000) at 10 research stations. Simple and combined analysis of variance revealed significant genetic differences between yield genotypes for grain yield. The results of combined analysis of variance indicated that genotypic and genotype were significant through interaction with environment. Therefore, different stability parameters including, environmental variance (S2i), environmental coefficient of variation (C.Vi), mean of variance of interaction (θi), interaction variance (θi), equivalence ( W2i), stability variance (σ2i), linear regression coefficient (bi, βi), mean of squares of deviation from regression (S2 di) and years within location MS for a genotype, averaging over all locations (MSy/l) were determined. Based on all the stability parameters, genotype 18 was known as the most stable one and genotypes 17 and 11 ranked lower. Genotype 5 with the highest yield was known to be the most adaptable one at fertile environments and is recommended for these locations. In addition, genotype 9 with good yield and low yield variance (1.58) and regression coefficient of less than 1 is suggested for unfertile locations.
G. Mohammadi Nejad, A. M. Rezai,
Volume 11, Issue 1 (4-2007)
Abstract
This research was conducted at Rsearch Farm of Isfahan University of Technology to evaluate yield stability of 9 Oat (Avena sativa L.) genotypes and Makooi barley, to determine the contribution of each environmental factor to genotype × environment interaction, and to find the most stable yield component in these genotypes. Four Canadian cultivars and 5 Turkish breeding lines were included in this experiment. Three dates of planting (12 Oct. 31 Oct. and 21 Nov.) and three sowing rates (300, 375 and 450 seed m2) were used as 6 environments. In each environment a randomized complete blocks design with 3 replications was used. Grain yield, No. of panicle/m2, No. of seed/ panicle, and 1000-grain weight were measured. Result of combined analysis of variance showed highly significant (P < 0.01) difference among genotypes for all the studied traits. Significante differences were observed among environments for all the characteristics except for 1000-grain weight. Grain yield and its components showed highly significant genotype × environment interaction ffects. The ratio of genotype × environment interaction sum of square to total sum of square for grain yield (22.37%) was higher than other traits. Stability analysis based on regression coefficient showed that Boyer cultivar and Line No.28 with nearly b=1 and more than average yields were the most stable genotypes. Pacer cultivar and Makooi Barley had specific adaptations with suitable and unsuitable environments, respcctively. Based on deviation mean square, Boyer cultivar was the most stable one among high yielding genotypes. Tai’s path analysis of genotype × environment interaction showed that V3 genotypic component (Seed weight) was the most effective component of stability and yield. Boyer with the highest V3 score was the highest yielding and stable genotype. According to environmtntal component of path analysis, fertilization stage and grain filling period were the most sensitive growth stages to environmental conditions. Therefor, it is not effective to evaluate genotype stability according to V2 component (seed/panicle). Finally, according to the result of this experiment Boyer with grain yield of 5.8 t/ha and stable response in all environments was selected as a suitable cultivar for breeding programs or introduction for commertial production.
H. Zali, S.h. Sabaghpour, E. Farshadfar, P. Pezeshkpour, M. Safikhani, R. Sarparast, A. Hashem Beygi,
Volume 11, Issue 42 (1-2008)
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.
