Critical review of data-driven breeding and selection on field-grown switchgrass (Panicum virgatum L.) as a bioenergy feedstock

Switchgrass is a highly promising bioenergy feedstock due to high biomass yield and ability to thrive on marginal lands. Enhancing switchgrass for biofuel production through data-driven breeding and selection is essential to meeting the growing need for sustainable and renewable energy sources. This review critically analyses current approaches and future directions in identifying key phenotypic traits, exploring genetic diversity, and developing predictive models to improve switchgrass. It underscores the importance of high-throughput phenotyping technologies and standardized protocols in pinpointing traits that enhance biofuel yield and conversion efficiency. The review discusses the necessity of comprehensive genotyping and sequencing to understand genetic diversity better and utilize beneficial traits in breeding programs. Moreover, the study highlights the potential of advanced machine learning algorithms and multi-dimensional data integration in creating strong predictive models for breeding decisions. This review provides a roadmap for future research and practical breeding strategies to optimize switchgrass as a bioenergy feedstock.