首页> 外文期刊>International Journal of Plant Breeding and Genetics >GGE-biplot analysis of multi-environment yield trials of barley (Hordeium vulgare L.) genotypes in Southeastern Ethiopia highlands.
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GGE-biplot analysis of multi-environment yield trials of barley (Hordeium vulgare L.) genotypes in Southeastern Ethiopia highlands.

机译:埃塞俄比亚东南部高地大麦(Hordeium v​​ulgare L.)基因型的多环境产量试验的GGE谱图分析。

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摘要

The present study was conducted on 18 food barley genotypes across 11 environments in randomized complete block design with four replications in Bale highlands of Southeastern Ethiopia from 2001-2003 Bona cropping season with the objective of evaluating yield performance of barley genotypes and identification of mega environments. GGE (i.e., G=genotype and GE=genotype by environment, interaction) biplot methodology was used for graphical display of yield data after subjecting the genotypic means of each environment to GGE Biplot software. The analysis of variance revealed that environment accounted for 76.7% of the total variation while G and GE-interaction explained 2.3 and 9.7%, respectively. The first two principal components (PC1 and PC2) were used to display a two-dimensional GGE biplot. Thus, genotypic PC1 scores >0 classified the high yielding genotypes while PC1 scores <0 identified low yielding genotypes. Unlike genotypic PC1, genotypic PC2, scores near zero showed stable genotypes whereas large PC2 scores discriminated the unstable ones. The environmental PC1 were related to cross over type of GEI while PC2 scores were associated with non cross over GEI. Genotypes (A, G, E, H, L, B and R) were found to be desirable in high yielding and stability. The 11 test environments in the highlands of Bale were divided in to two distinct mega environments (Mega-1 and 2). Mega-1 constituted environments such as E1 (Sinana-01), E2 (Gassara-01), E3 (Sinja-01), E4 (Sinana-02), E5 (Gassara-02), E6 (Sinja-02), E8 (Sinana-03), E9 (Gassara-03) and E10 (Sinja-03) while Biftu cultivar being the best winner, on the other hand, Mega-2 contained two environments, E7 (Upper Dinsho-02) and E11 (Upper Dinsho-03) while shage cultivar being the best winner. The results of this study indicated that breeding for specific adaptation should be taken as a breeding strategy in Bale highlands to exploit positive GEI to increase production and productivity of barley.Digital Object Identifier http://dx.doi.org/10.3923/ijpbg.2011.59.75
机译:本研究是在2001-2003年 Bona 种植季节的埃塞俄比亚东南部的Bale高地上,在11个环境中的18种食物大麦基因型上进行的随机完整区组设计,其中四个重复,目的是评估大麦的产量表现。大型环境的基因型和鉴定。在将每种环境的基因型均值经过GGE Biplot软件处理后,使用GGE(即G =基因型,GE =基因型,环境,相互作用)双图法对产量数据进行图形显示。方差分析表明,环境占总变化的76.7%,而G和GE相互作用分别解释了2.3和9.7%。前两个主要成分(PC1和PC2)用于显示二维GGE双线图。因此,基因型PC1得分> 0将高产基因型分类,而PC1得分<0则鉴定为低产量基因型。与基因型PC1和基因型PC2不同,接近零的分数表示稳定的基因型,而较大的PC2分数则区分了不稳定的基因型。环境PC1与GEI的交叉类型相关,而PC2得分与非交叉的GEI相关。发现基因型(A,G,E,H,L,B和R)具有高产量和稳定性。贝尔高地的11个测试环境分为两个不同的大型环境(Mega-1和2)。 Mega-1构成的环境,例如E1(Sinana-01),E2(Gassara-01),E3(Sinja-01),E4(Sinana-02),E5(Gassara-02),E6(Sinja-02),E8 (Sinana-03),E9(Gassara-03)和E10(Sinja-03),而 Biftu 品种是最佳获胜者,而Mega-2则包含两个环境,即E7(上丁绍) -02)和E11(Dinsho-03上层),而 shage 品种则是最佳赢家。这项研究的结果表明,应将适合特殊适应的育种作为大包高地的育种策略,以利用积极的GEI来提高大麦的产量和生产力。数字对象标识符http://dx.doi.org/10.3923/ijpbg。 2011.59.75

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