LAUSR

Spatial variation and genotype by environment (GxE) interaction are common in varietal selection field trials and pose a significant challenge for plant breeders when comparing the genetic potential of different varieties. Efficient statistical methods must be employed for the evaluation of finger millet breeding trials to accurately select superior varieties that contribute to agricultural productivity. The objective of this study was to improve selection strategies in finger millet breeding in Ethiopia through modeling of spatial field trends and the GxE interaction. A dataset of seven multi-environment trials (MET) conducted in randomized complete block design (RCBD) with two replications laid out in rectangle (row x column) arrays of plots was used in this study. The results revealed that, under the linear mixed model, the spatial and factor analytic (FA) models were efficient methods of data analysis for this study, and this was demonstrated with evidence of heritability measure. We found two clusters of correlated environments that helped to select superior and stable varieties through ranking average Best Linear Unbiased Predictors (BLUPs) within clusters. The first cluster was chosen because it contained a greater number of environments with high heritability. Based on this cluster, Bako-09, 203439, 203325, and 203347 were the top four varieties with relatively high yield performance and stability across correlated environments. Hence, scaling up the use of this efficient analysis method will improve the selection of superior finger millet varieties.