Use of multi-trait principal component selection index to identify fall armyworm (Spodoptera frugiperda) resistant maize genotypes

The Fall armyworm (FAW), <i>Spodoptera frugiperda</i> (J. E. Smith) invaded sub-Saharan Africa (SSA) in 2016 and has since become prevalent in many countries, causing significant maize grain yield losses and reduced grain quality. Breeding for host plant resistance to FAW requires improving multiple traits, complicating selection. This study evaluated the use of principal component (PC)-based multi-trait selection indices to identify FAW resistant maize genotypes. A total of 192 maize hybrids alongside four commercial hybrids, were evaluated over four seasons under artificial FAW infestation. Data on FAW leaf feeding damage (LD) at 7, 14, and 21 days after infestation, and ear damage (ED), ear rot (ER), and grain yield (GY) were recorded. The data were subjected to analysis of variance and PC analysis, and results used to construct two economic weight-free selection indices: PC1-based index (PC1BI) and PC2-based index (PC2BI). Broad-sense heritability estimates were 0.59 to 0.73 for LD, and 0.69 for GY. The two PCs explained 97.1% of the variation among the hybrids. PC1BI, with higher loadings for the leaf feeding damage traits, showed the larger desired gains for these traits (-2.92 to -3.84%) and GY (19.9%), making it a superior index to PC2BI. PC1BI identified six promising hybrids with GY above the cutoff of 7.0 t ha^-1 for selection under FAW infestation. PC2BI exhibited larger gains for ED (-11.1%) and ER (-45.4%). The index-based selected hybrids consistently outperformed the commercial hybrid checks. The PC-based indices have the potential to serve as valuable tools for breeders to maximize selection gains; however, modifications are necessary to incorporate other desirable agronomic and adaptive traits.