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AMMI analysis retains most of the genotype x environment interaction pattern in the first interaction principal component axis (IPCA) resulting from the singular value decomposition (SVD) of the non-additive effects matrix, while most of the random error has been retained as noise in the last IPCAs (Balestre et al., 2009; Gauch, 2013; Adjebeng et al., 2017; Bocianowski et al., 2019; Veenstra et al., 2019). Moreover, under AMMI analysis usage, there would be a biased interpretation regarding the stability of the genotypes for instances with the low proportion of the variance explained by first interaction principal component IPCA1 (Zali et al., 2012; Bornhofen et al., 2017; Oyekunle et al., 2017). Weighted Average of Absolute scores (WAASB), the quantitative stability measure was found to be an important statistical tool for identifying highly productive and broadly adapted genotypes (Olivoto, 2018). The genotype with the lowest WAASB value is considered the most stable, that is, the one that deviates least from the average performance across environments (Olivoto, 2019). For the selection of promising genotypes, a superiority index, i.e. combine high yield and stability at the same time allowed weighting mechanism for yield and stability (WAASB) (Olivoto et al., 2019). The present study was planned to validate the relationships between WAASBY and other stability measures, as per AMMI model, of wheat genotypes evaluated under multi environmental trials in the Central Zone of India under restricted irrigated timely sown trials in recent past.

journal of applied and natural science

Simultaneous application of AMMI measures and yield for stability analysis of wheat genotypes evaluated under irrigated late sown conditions of Central Zone of India