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Assessment of Stability, Adaptability and Yield Performance of Bread Wheat (Triticum Aestivum L.) Cultivars in South Estern Ethiopia

机译:埃塞俄比亚南部面包小麦品种(Triticum Aestivum L.)的稳定性,适应性和产量表现的评估

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The success of crop improvement and production activities can be enhanced with scientific information generated form genotype-environment interactions. GEI reduces the association between phenotype and genotype which result in relative ranking and stability differences of genotypes across environments. This study were conducted with the objective to identify stable, and adaptable bread wheat genotypes under various environments. Eighteen genotypes were tested across nine environments for two years on randomized block design of three replication. Plot size of 1.2 m × 2.5 m and 20cm spacing between rows were used. All recommended agronomic practices and managements were applied uniformly. Data were collected on plot basis and converted to ton ha-1. and analyzed with appropriate statistical software for stability parameters. Combined analysis over nine environments showed, variety Tuse (HAR-1407) ranked first in mean yield(3.11ton × ha-1), and K-6295–4A ranked second (3.01 ton × ha-1) and Dashen came third(2.98 ton ha-1). Analysis of AMMI model showed that the first principal component, PCA 1 explained 53.72% of the interaction sum of squares while the second principal component, PCA 2 explained 17.61% interaction sum of squares. Ecovalence(Wi) analysis showed that G2 (Sofumar(HAR-1889)), G4 (Kubsa(HAR-1685)), G5 (Tura(HAR-1407)), G7 (Galema (HAR-604)), G12 (Wabe (HAR-710)), almost equally the lowest ecovalence that evidenced less fluctuation across environment and found to be stable.
机译:基因型与环境相互作用产生的科学信息可以增强作物改良和生产活动的成功。 GEI减少了表型和基因型之间的关联,这导致了整个环境中基因型的相对排名和稳定性差异。进行这项研究的目的是确定在各种环境下稳定,适应性强的面包小麦基因型。在三个复制的随机区组设计中,在九个环境中测试了十八种基因型,为期两年。地块尺寸为1.2 m×2.5 m,行间距为20cm。所有推荐的农艺实践和管理均统一应用。以地块为基础收集数据,并转换为吨ha -1 。并使用适当的统计软件分析稳定性参数。对九种环境的综合分析显示,品种Tuse(HAR-1407)的平均产量排名第一(3.11ton×ha -1 ),而K-6295-4A排名第二(3.01 ton×ha ) > -1 )和大神排在第三位(2.98吨公顷 -1 )。对AMMI模型的分析表明,第一主成分PCA 1解释了相互作用的平方和的53.72%,而第二主成分PCA 2解释了相互作用的平方和的17.61%。 Ecovalence(Wi)分析显示,G2(Sofumar(HAR-1889)),G4(Kubsa(HAR-1685)),G5(Tura(HAR-1407)),G7(Galema(HAR-604)),G12(Wabe) (HAR-710)),几乎是最低的生态价,这表明跨环境的波动较小,并且很稳定。

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