The growing interest in high‐biomass sorghum (Sorghum bicolor L. Moench), hereafter referred to as sorghum, as a bioenergy feedstock in the United States requires an understanding of geographical adaptation to identify the most suitable hybrids for the Midwest. In this study, 13 sorghum hybrids (H1–H13) were evaluated for biomass yield potential in central and southern IL over two growing seasons (2022 and 2023). In addition to biomass yield, the effects of nitrogen (N) fertilization on yield, nutrient removal (N, P, and K), and feedstock composition (cellulose, hemicellulose, lignin, and soluble fractions) were determined to identify the best‐performing sorghum hybrid across environmental gradients. The experimental design was a split‐plot arrangement within a randomized complete block design with four replications at each of two locations: N rates (0 and 112 kg‐N ha⁻¹) as a whole plot factor and 13 sorghum hybrids as a subplot factor. As a result, complex genotypes (13 hybrids) by environment (2 sites and 2 years) and management (2 N rates) interactions were observed in biomass yield. The best hybrids at both sites were H1 (ATx2932/F10702_PSL) and H13 (TX08001), which were very photoperiod sensitive (PS). These hybrids produced superior biomass yield, and they also exhibited less nutrient removal and high energy‐rich feedstock compositions (cellulose, hemicellulose, and lignin). Biomass yield potential was associated with morphological and phenological traits according to environmental conditions. Low‐yielding hybrids were short‐stature (H5 and H6) with pollinators (F10801_PSL‐3dw and F10805_PSL‐3dw) that are recessive at the Dw3 locus. Moderate PS hybrids (H7, H8, H11, and H12) that produced grain panicles at harvest showed high biomass yield plasticity and excessive nutrient removal as they accumulated high K concentrations in biomass tissues and high N and P in grain panicles.

Innovative methods for estimating commercial-scale switchgrass yields and feedstock quality are essential to optimize harvest logistics and biorefinery efficiency for sustainable aviation fuel production. This study utilized vegetation indices (VIs) derived from multispectral images to predict biomass yield and lignocellulose concentrations of advanced bioenergy-type switchgrass cultivars (“Liberty” and “Independence”) under two N rates (28 and 56 kg N ha⁻¹). Field-scale plots were arranged in a randomized complete block design (RCBD) and replicated three times at Urbana, IL. Multispectral images captured during the 2021–2023 growing seasons were used to extract VIs. The results show that linear and exponential models outperformed partial least square and random forest models, with mid-August imagery providing the best predictions for biomass, cellulose, and hemicellulose. The green normalized difference vegetation index (GNDVI) was the best univariate predictor for biomass yield (R² = 0.86), while a multivariate combination of the GNDVI and normalized difference red-edge index (NDRE) enhanced prediction accuracy (R² = 0.88). Cellulose was best predicted using the NDRE (R² = 0.53), whereas hemicellulose prediction was most effective with a multivariate model combining the GNDVI, NDRE, NDVI, and green ratio vegetation index (GRVI) (R² = 0.44). These findings demonstrate the potential of UAV-based VIs for the in-season estimation of biomass yield and cellulose concentration.