Danakumara Thippeswamy, Kumar Tapan, Kumar Neeraj, Patil Basavanagouda Siddanagouda, Bharadwaj Chellapilla, Patel Umashankar, Joshi Nilesh, Bindra Shayla, Tripathi Shailesh, Varshney Rajeev Kumar, Chaturvedi Sushil Kumar
ICAR-Indian Agricultural Research Institute, New Delhi 110012, India.
International Centre for Agricultural Research in the Dry Areas, Sehore 466113, Madhya Pradesh, India.
Plants (Basel). 2023 Oct 26;12(21):3691. doi: 10.3390/plants12213691.
Identifying a congenially targeted production environment and understanding the effects of genotype by environmental interactions on the adaption of chickpea genotypes is essential for achieving an optimal yield stability. Different models like additive main effect and multiplicative interactions (AMMI 1, AMM2), weighted average absolute scores of BLUPs (WAASB), and genotype plus genotype-environment (GGE) interactions were used to understand their suitability in the precise estimation of variance and their interaction. Our experiment used genotypes that represent the West Asia-North Africa (WANA) region. This trial involved two different sowing dates, two distinct seasons, and three different locations, resulting in a total of 12 environments. Genotype IG 5871(G1) showed a lower heat susceptibility index (HSI) across environments under study. The first four interactions principal component axis (IPCA) explain 93.2% of variations with significant genotype-environment interactions. Considering the AMMI stability value (ASV), the genotypes IG5862(G7), IG5861(G6), ILC239(G40), IG6002(G26), and ILC1932(G39), showing ASV scores of 1.66, 1.80, 2.20, 2.60, and 2.84, respectively, were ranked as the most stable and are comparable to the weighted average absolute scores of BLUPs (WAASB) ranking of genotypes. The which-won-where pattern of genotype plus genotype-environment (GGE) interactions suggested that the target environment consists of one mega environment. IG5866(G10), IG5865(G9), IG5884(G14), and IG5862(G7) displayed higher stability, as they were nearer to the origin. The genotypes that exhibited a superior performance in the tested environments can serve as ideal parental lines for heat-stress tolerance breeding programs. The weighted average absolute scores of BLUPs (WAASB) serve as an ideal tool to discern the variations and identify the stable genotype among all methods.
确定适宜的目标生产环境并了解基因型与环境互作对鹰嘴豆基因型适应性的影响,对于实现最佳产量稳定性至关重要。使用了不同的模型,如加性主效应和乘积互作模型(AMMI 1、AMM2)、最佳线性无偏预测(BLUP)的加权平均绝对得分(WAASB)以及基因型加基因型 - 环境(GGE)互作模型,以了解它们在精确估计方差及其互作方面的适用性。我们的实验使用了代表西亚 - 北非(WANA)地区的基因型。该试验涉及两个不同的播种日期、两个不同的季节和三个不同的地点,总共形成了12种环境。基因型IG 5871(G1)在研究的各个环境中表现出较低的热敏感指数(HSI)。前四个互作主成分轴(IPCA)解释了93.2%的变异,且存在显著的基因型 - 环境互作。考虑到AMMI稳定性值(ASV),基因型IG5862(G7)、IG5861(G6)、ILC239(G40)、IG6002(G26)和ILC1932(G39)的ASV得分分别为1.66、1.80、2.20、2.60和2.84,被列为最稳定的基因型,并且与最佳线性无偏预测(BLUP)的加权平均绝对得分(WAASB)对基因型的排名相当。基因型加基因型 - 环境(GGE)互作的“哪一个在何处胜出”模式表明目标环境由一个大环境组成。IG5866(G10)、IG5865(G9)、IG5884(G14)和IG5862(G7)表现出较高的稳定性,因为它们更接近原点。在测试环境中表现优异的基因型可作为耐热胁迫育种计划的理想亲本系。最佳线性无偏预测(BLUP)的加权平均绝对得分(WAASB)是在所有方法中辨别变异并识别稳定基因型的理想工具。