Dataset for <b>Fast-tracking Miscanthus Breeding: High-Spatial Resolution Aerial Imagery as a Cost-Effective Tool for Early Yield Prediction</b>

For the PA scheme, across models and phenotyping levels (70%, and 30%) correlations ranged 0.22-0.67 (Msa), 0.52-0.92 (Msi.C), and -0.14-0.93 (0.93) with the models including <b>s</b> term always performing the best in contrast to <b>g</b> models. Under the EPA scheme, across populations and for the different phenotyping levels, the best results were obtained with those models that included the <b>s</b> component and these varied between 0.61 and 0.81 (30%), 0.72 and 0.84 (70%), and 0.7 and 0.81 (100%) while for those models only including genomic information ranged between -0.01 and 0.32 (30%), 0.05 and 0.44 (70%), and 0.67 to 0.83 (100%).

By phenotyping only 30% of the families, the <b>s</b> model was able to capture 68% (Msa), 71%, (Msi.C) and 82% (Msi.S) of the genetic gain achieved through the joint performance of all years. The integration of high-throughput phenotyping using UAVs significantly improved the accuracy of early-stage yield predictions in Miscanthus. Models including the <b>s</b> component as main and interaction effects consistently outperformed strictly genomic models, particularly in scenarios with limited phenotypic data.</p>