Multivariate modeling of nutritional and yield traits in extra‐early provitamin A quality protein maize

Abstract Understanding genetic variability and the relationships among nutritional and agronomic traits is essential for guiding maize ( Zea mays L.) biofortification strategies. In this study, we evaluated 30 extra‐early provitamin A quality protein maize (PVA‐QPM) inbreds and two commercial checks. We measured grain yield and nutritional composition, including β‐carotene, total carotenoids, lysine, tryptophan, iron, zinc, and starch quality. The field experiments were conducted under rainfed conditions at Oke‐Oyi, Nigeria, during the 2023 and 2024 cropping seasons. Experiments used a randomized complete block design with three replications. We employed multivariate analyses, including principal component analysis (PCA) and hierarchical clustering, to assess trait variability, examine interrelationships, and classify genotypes based on their nutritional and agronomic performance. The results showed significant variation among the inbreds. TZEIORQ 19, TZEIORQ 57, and TZEIORQ 29 had the highest levels of PVA carotenoids. TZEIORQ 7 and TZEIORQ 19 were superior in lysine and tryptophan content. Iron‐ and zinc‐rich genotypes, including TZEIORQ 19 and TZEIORQ 7, showed strong potential for reducing micronutrient deficiencies. PCA revealed that the first principal component (PC1) captured most of the variation in key nutritional traits. This helped to clarify the relationships between traits. Using multivariate statistical methods made it possible to clearly differentiate elite PVA‐QPM inbreds that combine high yield with enhanced nutritional quality. These genotypes are promising candidates for large‐scale planting and biofortification programs to improve dietary quality and food security in sub‐Saharan Africa.