利用GGE双图分析评价大豆(Glycine max L. Merril)候选品系的适应性和产量稳定性

H. Babaei, ,. N. Razmi, S. Raeisi, H. Sabzi
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引用次数: 1

摘要

刘建军,张建军,张建军,张建军。2020。利用GGE双图分析评价大豆(Glycine max L. Merril)候选品系的适应性和产量稳定性。作物学报,22(2):183-197。(波斯)。选择具有高产和产量稳定的适应基因型是大豆育种计划的目标。评价大豆良种的适应性和产量稳定性。威廉姆斯作为检验采用随机完全区组设计进行评估,在伊朗的卡拉吉、戈尔根、莫汉和霍拉马巴德四个地点进行了四个重复试验,时间为2013年和2014年两个生长季节。采用GGE双标图分析方法评价其适应性和产量稳定性。综合方差分析表明,年份、位置、基因型、年份×位置、年份×基因型、位置×基因型和基因型×位置×年互作效应显著。年份、地理位置和基因型方差对总方差的贡献分别为0.01、0.60和0.02,表明地理位置方差的贡献较大。PC1和PC2的前两个组成部分解释了基因型和基因型×环境(G + GE)总观察变异的58%。在这项研究中,确定了三个大型环境。第一个大型环境包括:E2 (Karaj 2014)、E5 (Moghan 2013)和E8 (Gorgan 2014), G16是该大型环境中的优势基因型。第二个大型环境包括:E3(2013年的霍拉马巴德)和E4(2014年的霍拉马巴德),G8是该大型环境中的优越基因型。第三个大环境包括E1 (Karaj 2013)和E7 (Gorgan 2013),其中G17为该大环境的优势基因型。双图分析显示基因型为G17 (L85-3059),体重2702 kg。G16 (L12/Chaleston × Mustang)重量为2750公斤。Ha是高适应性基因型,种子产量高,产量稳定。E7环境(Gorgan, 2013)在大豆基因型的区分能力方面是最理想的环境,也是目标环境的最佳代表。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluation of adaptability and seed yield stability of soybean (Glycine max L. Merril) promising lines using GGE biplot analysis
Babaei, H. R., N. Razmi, S. Raeisi and H. Sabzi. 2020. Evaluation of adaptability and seed yield stability of soybean (Glycine max L. Merril) promising lines using GGE biplot analysis. Iranian Journal of Crop Sciences. 22(2): 183-197. (In Persian). Selection of adapted genotypes with high seed yield and yield stability is the goal of soybean breeding programs. To evaluate the adaptability and seed yield stability of soybean promising lines, 19 promising lines and cv. Williams as check were evaluated using randomized complete block design with four replications in four locations: Karaj, Gorgan, Moghan and Khoramabad in Iran during two growing seasons (2013 and 2014). GGE biplot analysis was employed to evaluate the adaptability and seed yield stability. Combined analysis of variance showed thatyear, location, genotype, year × location, year × genotype, location × genotype and genotype × location × year interaction effects were significant on studied traits. The contribution of year, location and genotype variance to total variance was 0.01, 0.60 and 0.02, respectively, indicating considerable contribution of location variance. The first two components of PC1 and PC2 explained overall 58% of total observed variation of genotype and genotype × environment (G + GE). In this study, three mega-environments were identified. The first mega-environment included: E2 (Karaj 2014), E5 (Moghan 2013) and E8 (Gorgan 2014) and G16 was the superior genotype in this mega-environment. The second mega-environment included: E3 (Khorramabad 2013) and E4 (Khorramabad 2014) and G8 was the superior genotype in this mega-environment. Third megaenvironment consisted: E1 (Karaj 2013) and E7 (Gorgan 2013) and G17 was the superior genotype in this megaenvironment. Biplot analysis showed that genotypes: G17 (L85-3059) with 2702 kg.ha and G16 (L12/Chaleston × Mustang) with 2750 kg.ha were highly adapted genotypes with high seed yield and yield stability. The E7 environment (Gorgan, 2013) was the most desirable environment in respect to its discriminating ability among soybean genotypes and the best representative of the target environments.
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