Canopy Architectural and Physiological Traits for Optimizing Maize Yield under High Planting Density

Muhammad Azrai^1*Bunyamin Zainuddin¹Elkawakib Syam'un¹Yunus Musa¹Roy Efendi²Slamet Bambang Priyanto²Nining Nurini Andayani²Muhammad Aqil²

• ¹Hasanuddin University, Makassar, Indonesia
• ²Research Center for Food Crops, National Research and Innovation Agency (BRIN), Republic of Indonesia, Jakarta, Indonesia

Despite substantial increases in planting density to enhance maize grain yield, productivity at the plant level has remained stagnant. Although leaf-related traits have been extensively studied in commercial hybrids, they remain underexplored in tropical maize breeding programs. This study aimed to: (1) investigate genotype-specific yield responses under standard, intermediate, and intensive planting densities; (2) evaluate correlations among key leaf physiological traits across density changes; and (3) identify resilient hybrids for high-density cultivation through a multi-trait genotype-ideotype distance index (MGIDI) assessment. The research was conducted at the Bajeng Experimental Station in Indonesia using a split-plot randomized complete block design with three replications. The trial assigned 11 maize genotypes and two upright-leaf commercial hybrids to the main plots, with subplots testing three densities: standard (±71,000 plants ha⁻¹), intermediate (±81,000 plants ha⁻¹), and intensive (±95,000 plants ha⁻¹). The results indicated that genotype (G) and population density (D) significantly influenced yield, while the G × D interaction had no significant effect (p = 0.2981). Intermediate density achieved the highest average yield (12.85 t ha⁻¹), surpassing both standard (11.54 t ha⁻¹) and intensive (11.79 t ha⁻¹) planting densities. The MGIDI model identified hybrids G4, G2, and G5 as broadly adaptable across densities, demonstrating stability in intermediate and intensive population densities. The MGIDI framework is recommended for integration into genotype-environment interaction analyses, enabling targeted identification of stress-resilient cultivars by balancing trait trade-offs.