The study evaluated whether integrating genomic, transcriptomic, and drone-derived phenomic data improves prediction of 129 maize traits across nine environments, using both linear (rrBLUP) and nonlinear (SVR) models. Multi-omics models consistently outperformed single-omics models, with transcriptomic data especially enhancing cross‑environment predictions and capturing genotype‑by‑environment interactions. The results highlight the added value of combining transcriptomics and phenomics with genotypes for more accurate and generalizable trait prediction in maize.

The study mapped the macroscopic and cellular development of maize leaves and internodes, revealing a shared growth design with organ‑specific timing. Using high‑resolution spatiotemporal transcriptome profiling of 272 tissue samples under well‑watered and drought conditions, the authors generated a searchable expression atlas and identified conserved and organ‑specific gene regulatory patterns, including genes linked to leaf angle and vascular development. This resource advances understanding of shoot organ development and drought response for targeted trait engineering in maize.