Ethiopia is a key maize producer in Africa. Over the previous two decades, Ethiopia's maize sector has seen tremendous development. Farmers in Ethiopia demand a continual supply of novel and improved varieties to satisfy their ever-changing production and marketing difficulties. Breeders can no longer function without the analysis of multi-environment trials (MET) for varietal evaluation. To reliably choose better varieties that boost agricultural production, efficient statistical methods for maize variety evaluation must be used. This study used multiplicative mixed models to analyze data from multi-environment trials in order to identify outstanding maize varieties based on yield performance. In this study, 32 maize varieties, including four checks, were sown across seven major maize growing areas in Ethiopia using RCB design, with three replications during the main cropping season in 2020. The findings showed that factor analytic models were a successful approach for maize MET data analysis under the linear mixed model. The examined FA models have better data fitting, which significantly improves heritability. SXM1910008 and 3XM1920126 showed good yield performance over correlated locations, including Ambo, Bako, Hawasa, and Wondogenet, and were therefore identified as potentially useful stable genotypes with a wide range of adaptability. This is because the improved analysis technique we used here showed that correlated locations were the basis for genotype selection. Data from multi-environment trials can be analyzed to provide a more reliable framework for evaluating maize varieties, giving breeders more confidence to select superior varieties for a wide range of environments. This can be done by using more efficient statistical models. In order to improve the selection of better varieties in the maize breeding program, it is vital to increase the usage of this efficient analysis technique.
factor analytic model, MET analysis; BLUP, mixed model, maize
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