AMMI and GGE Biplot Analyses for Mega Environment Identification and Selection of Some High-Yielding Cassava Genotypes for Multiple Environments

IF 1.5 Q2 AGRONOMY International Journal of Agronomy Pub Date : 2023-04-04 DOI:10.1155/2023/6759698
Berhanu Bilate Daemo, Derbew Belew Yohannes, Tewodros Mulualem Beyene, Wosene Gebreselassie Abtew
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引用次数: 4

Abstract

Cassava (Manihot esculenta Crantz) is a staple food and generates income for smallholder farmers in southern Ethiopia. The performance of cassava genotypes varies in different growing environments; thus, the evaluation of genotypes tested in various environments plays an essential role in developing strategies to delineate environments, explore unstable genotypes in target environments, and identify stable genotypes for multiple environments. In this regard, there needs to be more information on the identification of mega-environments and stable genotypes with high yields for wide adaptation. Thus, this study aimed to identify mega-environment and high-yielding cassava genotypes for multiple environments using AMMI and GGE biplots. A total of 25 genotypes were evaluated in six environments using a RCBD during the 2020–2021 cropping season. The AMMI analysis of variances revealed that environments, genotypes, and genotype-environment interaction had a significant ( P ≤ 0.001 ) influence on cassava fresh storage root yield (t·ha−1), showing genetic variability among genotypes by changing environments. The genotype-by-environment interaction showed a 61.36% contribution to the total treatment SS variation, while the environment and genotype effects explained 28.16% and 10.48% of the total treatment SS, respectively. IPCA1 and IPCA2 accounted for 33.42% and 23.5% of the GE interactions SS, respectively. The GGE biplot showed that the six environments used in this study were delineated into three mega-environments, namely, the first (Tarcha and Disa), the second (Wara and Areka), and the third (Jimma and Bonbe). Those mega-environments could be helpful for genotype evaluation and effective breeding. The GGE biplot indicated that the vertex genotypes were G16, G17, and G25. They are regarded as specifically adapted genotypes since they are more responsive to environmental change. The GGE biplot also revealed that Tarcha was ideal, having the most discriminating and representative environment, while G10 was the ideal and the overall winning genotype for the current study. Moreover, the genotypes G10 and G14 were identified as being the most stable, with a higher fresh storage root yield than the grand mean. Thus, G10 and G14 were selected as superior genotypes that could be promoted to advanced yield trials to develop stable cultivars with better storage root yield of cassava.
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大环境下的AMMI和GGE双标分析多环境下木薯高产基因型的鉴定与筛选
木薯(Manihot esculenta Crantz)是埃塞俄比亚南部的一种主食,为小农户带来收入。木薯基因型在不同的生长环境中表现不同;因此,对在各种环境中测试的基因型的评估对于制定描述环境的策略、探索目标环境中的不稳定基因型以及确定多种环境中的稳定基因型起着至关重要的作用。在这方面,需要有更多的信息来识别大规模环境和高产的稳定基因型,以便广泛适应。因此,本研究旨在使用AMMI和GGE双地块确定多种环境下的巨型环境和高产木薯基因型。在2020-2021年种植季节,使用RCBD在六个环境中对总共25种基因型进行了评估。方差AMMI分析表明,环境、基因型和基因型-环境相互作用对木薯鲜贮藏根产量(t·ha−1)有显著影响(P≤0.001),表现出不同基因型因环境变化而产生的遗传变异。基因型与环境的相互作用对总处理SS变异的贡献率为61.36%,而环境和基因型效应分别解释了总处理SS的28.16%和10.48%。IPCA1和IPCA2分别占GE相互作用SS的33.42%和23.5%。GGE双图显示,本研究中使用的六个环境被划分为三个巨型环境,即第一个(Tarcha和Disa)、第二个(Wara和Areka)和第三个(Jimma和Bonbe)。这些巨型环境可能有助于基因型评估和有效育种。GGE双谱图显示顶点基因型分别为G16、G17和G25。它们被认为是特别适应的基因型,因为它们对环境变化更敏感。GGE双标还显示,Tarcha是理想的,具有最具辨别力和代表性的环境,而G10是当前研究的理想和总体获胜基因型。此外,G10和G14基因型被认为是最稳定的,具有比总平均值更高的鲜贮藏根产量。因此,选择G10和G14作为优势基因型,可以推广到高级产量试验中,以开发出具有更好贮藏根产量的稳定木薯品种。
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来源期刊
CiteScore
3.60
自引率
5.30%
发文量
66
审稿时长
16 weeks
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