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Showing 2 results for Genotype × Environment Interaction Effect
Jamshid Moradpour, Hadi Ahmadi, Mahmoud Bagheri, Daryoush Goudarzi,
Volume 9, Issue 1 (9-2022)
Abstract
Eggplant (Solanum melongena L.) has a high genetic variation in Iran and there are many landraces of this crop in Iran. In the present study, 15 superior genotypes of eggplant which were selected from Minab landraces accompanying two superior mother landraces (totally 17 lines) were studied for two successive years in three regions of Iran including Minab, Karaj and Jiroft. The experiment was conducted in Randomized complete block design with three replications. Finally, total yield of both years was measured and the combined analysis was done and the best line(s) for different climates were introduced using evaluation the stability of the lines via AMMI and GGE biplot procedures. Based on the results of means comparison of yield in the studied lines in each region from average of two years, GHE12 line in Minab region, SA13 line in Jiroft region and AM4, SA15 and SA5 lines in Karaj region have higher fruit yield than the other lines. Based on the results of yield comparison of the examined genotypes in each region from the average of two years of testing, GHE12 genotype in Minab region, SA13 genotype in Jiroft region and AM4, SA15 and SA5 genotypes in Karaj region had acceptable yield compared to other genotypes. However, according to the results of special adaptability and stability analysis, Y genotype for Minab region, SA13 genotype for Jiroft region and AM4 genotype for Karaj region are recommended
Seyedeh Somayeh Mousavi, Omidali Akbarpour, Dr Tahmasb Hosseinpour,
Volume 10, Issue 1 (9-2023)
Abstract
In this research, 15 bread wheat genotypes along with Aftab variety as a control variety were implemented with 4 replications in the form of randomized complete block design for 3 crop years (2016-2019) at Sarab Chengai Station in Khorramabad. The likelihood ratio test (LRT) showed that the genotype-year interaction effect was significant for grain yield. Based on this, singular value analysis (SVD) was performed on the matrix of best linear unbiased predictions (BLUP) of genotype × year interaction to evaluate the stability of genotypes. The scree plot showed that the first principal component accounted for 71.7% and the second principal component accounted for 28.3% of the matrix changes resulting from the best unbiased predictions of the genotype interaction per year. The biplot of the first principal component of the environment against the nominal yield also showed that genotypes No. 9, 12 and 13 had a negligible contribution to the genotype × year interaction and had higher general stability. Also, the biplot of grain yield against the weighted average of absolute scores (WAASB) placed the genotypes in four regions, so that genotypes No. 15, 16, 12, 11, and 10 are in the fourth region due to high stability (low values WAASB) and magnitude of response variable (high performance) were identified as superior genotypes. The WAASBY index (weighted average of WAASB stability and performance) identified genotypes No. 15, 16, 12, 10, 11, 14, 9 and 4 as stable and high yielding genotypes. In general, based on WAASB and WAASBY indices and their comparison, genotypes 15, 16, 12, 11 and 10 were selected as the best genotypes that can be recommended for cultivation in similar climates.
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