Jiaojiao Ren

Plant architecture-related traits are key agronomic traits affecting crop growth and yield. To unravel the genetic architecture of plant height (PH), ear height (EH), tassel length (TL), and tassel primary branch number (TPBN), 379 DH lines derived from 21 maize hybrids were used for genome-wide association study (GWAS) and genomic selection (GS) analyses. Although plant architecture-related traits were significantly influenced by genotype and genotype-by-environment interactions, moderate to high broad-sense heritability was observed for PH (81.3%), EH (79.6%), TL (86.4%), and TPBN (82.5%). Using six different models for GWAS, seven unique SNPs on chromosomes 1, 2, and 3 were identified for PH, 92 unique SNPs located on chromosomes 1 to 9 were identified for EH, three unique SNPs on chromosome 6 were detected for TL, and 18 unique SNPs located on chromosomes 1, 4, 5, 8, and 10 were identified for TPBN at the p-value threshold of 7.42 × 10⁻⁶. A few hotspot genomic regions conferring plant architecture-related traits were identified, located in bins 2.07, 4.07, 8.03, 6.01, and 10.00. A total of 144 putative candidate genes were identified, which were enriched in endocytosis and lipid biosynthetic process, electron carrier activity, chloroplast stroma, and plastid stroma. The prediction accuracy evaluated through 5-fold cross-validation was 0.44 for PH, 0.43 for EH, 0.31 for TL, and 0.30 for TPBN. When the training population size (TPS) reached 60–70% or marker density (MD) reached 3000, the prediction accuracy tends to stabilize, indicating that the optimum size of TPS and MD were 60–70% and 3000 for GS, respectively. The highest prediction accuracy evaluated by using 30–5000 significant SNPs corresponding to the lowest p-value was 0.70 for PH, 0.85 for EH, 0.58 for TL, and 0.75 for TPBN, with an increase in accuracy of 59.1% to 150.0%. These results demonstrate that integrating GS with a subset of highly significant SNPs can substantially enhance prediction efficiency, thereby facilitating the selection of superior genotypes and accelerating the breeding of maize varieties with optimized plant architecture. This study has further elucidated the genetic basis of maize architecture-related traits and provided valuable information on how to implement GS to breed novel maize varieties with optimized plant types.

The leaf area index (LAI) is a direct indicator of crop canopy growth and serves as an indirect measure of crop yield. Unmanned aerial vehicles (UAVs) offer rapid collection of crop phenotypic data across multiple time points, providing crucial insights into the evolving dynamics of the LAI essential for crop breeding. In this study, the variation process of the maize LAI was investigated across two locations (XD and KZ) using a multispectral sensor mounted on a UAV. During a field trial involving 399 maize inbred lines, LAI measurements were obtained at both locations using a random forest model based on 28 variables extracted from multispectral imagery. These findings indicate that the vegetation index computed by the near-infrared band and red edge significantly influences the accuracy of the LAI prediction. However, a prediction model relying solely on data from a single observation period exhibits instability (R² = 0.34–0.94, RMSE = 0.02–0.25). When applied to the entire growth period, the models trained using all data achieved a robust prediction of the LAI (R² = 0.79–0.86, RMSE = 0.12–0.18). Although the primary variation patterns of the maize LAI were similar across the two fields, environmental disparities changed the variation categories of the maize LAI. The primary factor contributing to the difference in the LAI between KZ and XD lies in soil nutrients associated with carbon and nitrogen in the upper soil. Overall, this study demonstrated that UAV-based time-series phenotypic data offers valuable insight into phenotypic variation, thereby enhancing the application of UAVs in crop breeding.