Abstract

Switchgrass (Panicum virgatum L.) is noted for its high biomass yield and adaptability, making it a remarkable bioenergy source. Improving switchgrass for biofuel requires efficient collection and analysis of phenotype data. This review evaluates current methods and emphasizes the necessity of standardized, comprehensive data collection to enable reliable comparisons across studies. It examines the complex interplay between switchgrass genotypes and environmental conditions, advocating for extensive field tests and predictive models to create site-specific management strategies. The review emphasizes significant advancements in high-throughput phenotyping tools, including drones, hyperspectral imaging, and machine learning, which enhance data collection by increasing speed, accuracy, and detail. By integrating these cutting-edge technologies with a standardized methodology, this study establishes a framework for improving the efficiency and sustainability of biofuel production from switchgrass. It also offers practical recommendations for optimizing switchgrass as a bioenergy source.

Keywords:Switchgrass; Bioenergy feedstock; Phenotype data collection; High-throughput phenotyping; Genotype-by-environment interactions