Jokarfard Vahid, Rabiei Babak, Laki Ebrahim Souri, Börner Andreas
Department of Plant Production and Genetic Engineering, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran.
Department of Gene Bank, Institute of Plant Genetics and Crop Plant Research, Gatersleben, Germany.
Front Plant Sci. 2024 Nov 29;15:1487106. doi: 10.3389/fpls.2024.1487106. eCollection 2024.
The genotype × environment interaction is one of the effective factors in identifying and introducing cultivars with stable grain yield in different environments. There are many statistical methods for estimating genotype × environment interaction, among which AMMI and GGE-biplot analyses provide better and more interpretable results. The objective of this study was to assess the genotype × environment interaction, as well as the adaptability and stability of 40 quinoa genotypes. The experiment was carried out in a randomized complete block design with three replications in eight environments (four locations of Iran and two years). The AMMI analysis of variance showed that the main effects of genotype and environment, as well as the interaction effect of genotype × environment were significant on grain yield. Separation of genotype × environment interaction based on the principal component method showed that the first six principal components were significant and accounted for 47.6%, 22.5%, 9%, 7%, 6% and 4.3% of the genotype × environment interaction variance, respectively. Based on the AMMI model, genotypes G16, G19, G35, G30, G39, G24, and G18 were identified as high-yielding and stable genotypes with high general adaptability. In contrast, genotypes G36, G27, G38, G9, G28, G29, G23, G34, G13, and G12 were the most unstable genotypes in the studied environments. In GGE-biplot analysis, two mega-environments were identified, and genotypes G16, G19, G25, and G17 were also identified as high-yielding and stable genotypes for these environments. Also, based on the biplot diagram of the ideal genotype, genotypes G16, G19, G17, and G35 were the nearest genotypes to the ideal genotype. In total, the results of various analyses showed that the three genotypes G16 and G19 were the superior genotypes of this experiment in terms of grain yield and stability. These genotypes can be introduced as high-yielding and stable genotypes to the climatic conditions of the studied areas.
基因型×环境互作是鉴定和引进在不同环境下具有稳定籽粒产量品种的有效因素之一。有许多统计方法可用于估算基因型×环境互作，其中加性主效应乘积互作（AMMI）分析和基因型主效应与基因型×环境互作加性效应（GGE）双标图分析能提供更好且更具解释性的结果。本研究的目的是评估40个藜麦基因型的基因型×环境互作以及适应性和稳定性。试验采用随机完全区组设计，在8个环境（伊朗的4个地点和2年）中进行，重复3次。AMMI方差分析表明，基因型和环境的主效应以及基因型×环境互作效应在籽粒产量上均显著。基于主成分法对基因型×环境互作进行分离，结果表明前六个主成分显著，分别占基因型×环境互作方差的47.6%、22.5%、9%、7%、6%和4.3%。基于AMMI模型，基因型G16、G19、G35、G30、G39、G24和G18被鉴定为高产且稳定的基因型，具有较高的一般适应性。相比之下，基因型G36、G27、G38、G9、G28、G29、G23、G34、G13和G12是研究环境中最不稳定的基因型。在GGE双标图分析中，鉴定出两个大环境，基因型G16、G19、G25和G17也被鉴定为这些环境下的高产且稳定的基因型。此外，基于理想基因型的双标图，基因型G16、G19、G17和G35是最接近理想基因型的基因型。总体而言，各种分析结果表明，基因型G16和G19这三个基因型在籽粒产量和稳定性方面是本试验的优良基因型。这些基因型可作为高产且稳定的基因型引入到研究区域的气候条件下。
