<p>Earlymaturity and lowear position traits are important selective traits in maize breeding procedures, especially in northern China. To construct a genomic prediction model for selecting early-maturity and low-ear-position hybrids adapted to different environments, 34elite inbred lines were selected to produce 285 single-cross hybrids using a partial diallel cross design. These inbred lines constituted a mini-core collection of Chinese maize germplasm, comprising 18 inbred lines from the Stiff Stalk heterotic group and 16 inbred lines from the Non-Stiff Stalk heterotic group. The parental inbred lines were genotyped by sequencing, and the 285 hybrids were phenotyped for early-maturity and low-ear-position-related traits at seven locations in China. The results revealed significant variations in early maturity and low ear position traits among seven locations. These traits were highly correlated across locations, with high heritability ranging from 0.89 to 0.94, indicating that genetic factors primarily govern early maturity and ear position. The population structure showed that the hybrids were mainly composed of four subgroups. Among them, the Reid × Iodent subgroup exhibited the shortest growth period and the lowest ear position across the seven locations and showed relatively high genetic diversity. Correspondingly, the low general combining abilities of the inbred lines in both subgroups across the locations were also identified. The genomic prediction accuracy of these agronomic traits ranged from 0.26 to 0.89. Accuracy across seven locations ranged from 0.37 to 0.89. We also found that effective genomic prediction models can be set and developed using a training set comprising less than 16% of the entire dataset, covering both breeding aims. Genomic prediction achieved moderate to high accuracy with a small training set, demonstrating clear potential to streamline hybrid selection and enhance prediction stability.</p>