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Comparison of GGE biplot and AMMI analysis of multi-environment trial (MET) data to assess adaptability and stability of rice genotypes

机译:GGE双图和AMMI多环境试验(MET)数据的比较,以评估水稻基因型的适应性和稳定性

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摘要

Genotype × Environment (G×E) interaction and stability performance were investigated on paddy yield of eighteen rice genotypes and twelve locations using two well renowned statistical models; genotype main effect and G×E Biplot analysis (GGE) and additive main effects and multiplicative interaction (AMMI) analysis. The aim of this study was to elucidate the performance of some advance rice lines/genotypes at multiple locations in multi environment trials (METs) using GGE biplot and AMMI analyses. The results of GGE biplot and AMMI analyses performed over the data of paddy yield at multiple locations of two years 2014 and 2015 indicated that G×E interaction plays a crucial role in determining the performance of genetic material in METs. The results declare that GGE and AMMI not only provide easy and affective evaluation of genotypes into environment interactions in a number of locations but also a comprehensive understanding of the variability of the target locations. AMMI analyses for data of both years indicated that RRI 7 was the highest priority selected genotype for six locations, NIAB 1175 for four and RRI 3 for three locations. Dhokri and Kala Shah Kaku were the highest yielding, while Faisalabad and Dhokri were the most stable environments in 2014. Likewise, Faisalabad and PARC Islamabad were the highest yielding as well as most stable environments in 2015. Basmati 515 and PS 2 were the most favorable genotypes in 2014 and 2015, respectively for their high paddy yield and stability at all locations. The results further suggested that both models were useful and presented similar interpretations about MET data.
机译:利用两个著名的统计模型,研究了18种水稻基因型和12个位置的基因型×环境(G×E)相互作用和稳定性能。基因型主效应和G×E Biplot分析(GGE)和加性主效应和乘性相互作用(AMMI)分析。这项研究的目的是使用GGE双线图和AMMI分析来阐明在多环境试验(METs)中多个位置的一些先进水稻系/基因型的表现。 GGE双图和AMMI分析的结果是对2014和2015年的两个位置的多个位置的水稻产量数据进行的,表明G×E相互作用在决定METs中遗传物质的性能方面起着至关重要的作用。结果表明,GGE和AMMI不仅可以轻松,有效地评估基因型在许多位置与环境之间的相互作用,而且可以全面了解目标位置的变异性。 AMMI对两年数据的分析表明,RRI 7是六个位置的最高优先选择基因型,NIAB 1175是四个位置,RRI 3是三个位置。 2014年,Dhokri和Kala Shah Kaku的产量最高,而Faisalabad和Dhokri的产量最高。2015年,Faisalabad和PARC Islamabad的产量最高,也是最稳定的环境。Basmati 515和PS 2是最有利的基因型分别在2014年和2015年因其在所有地点的稻谷产量高和稳定性高而不同。结果进一步表明,这两个模型都是有用的,并对MET数据提供了类似的解释。

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