Performance assessment of wheat genotypes based on the superiority index using additive main and multiplicative interaction effects and BLUP analysis

A. Verma, G. Singh
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Abstract

The simultaneous use of additive main and multiplicative interaction effects (AMMI) and best linear unbiased predictors (BLUP) has been reflected in the multi-location evaluation of trials for number of crops. The additional advantages of both these approaches would be combined in superiority index (SI) to have an edge over the commonly used approaches. The promising wheat genotypes had been considered under multi location trails in Peninsular zone of India during the cropping seasons of 2018-2019 and 2019-2020. The highly significant environmental effects contributed 44.1% & 35.3% of total sum of squares in the AMMI analysis, 20.6% & 26.2% were augmented by G × E interaction, while 10.8% & 7.5% were contributed by the genotypes.Wheat genotypes of UAS3001, MACS6222, GW322, and DDW48 expressed their superiority in BLUP values. Superiority indexes and adaptability measures had identified WHD964 and DDW48 genotypes for the second year of study. More than 75% variations among the considered measures were due to the first two interaction principal components (IPCA’s) under Biplot analysis. Number of superiority index measures were clustered with adaptability measures in the same quadrant. Superiority index, the weighted measure of yield and consistent performance of genotypes would be more appropriate for stability and adaptabilities studies.
基于加性主效应和乘法互作效应的优势指数和BLUP分析的小麦基因型性能评价
同时使用加性主效应和乘法相互作用效应(AMMI)以及最佳线性无偏预测因子(BLUP)已反映在作物数量试验的多地点评估中。这两种方法的额外优势将在优势指数(SI)中结合起来,以比常用方法具有优势。在2018-2019年和2019-2020年的种植季节,在印度半岛地区的多地点试验中考虑了有前景的小麦基因型。在AMMI分析中,高度显著的环境效应占总平方和的44.1%和35.3%,G×E相互作用增加了20.6%和26.2%,而基因型贡献了10.8%和7.5%。UAS3001、MACS6222、GW322和DDW48的小麦基因型在BLUP值上表现出优势。在第二年的研究中,优势指数和适应性措施确定了WHD964和DDW48基因型。所考虑的措施之间超过75%的差异是由于双标分析下的前两个相互作用主成分(IPCA)造成的。在同一象限中,优势指数测度的数量与适应性测度进行聚类。优势指数、产量和基因型一致性表现的加权衡量标准更适合稳定性和适应性研究。
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