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Background The structural characteristics of whole sorghum kernels are known to affect end-use quality, but traditional evaluation of this structure is two-dimensional (i.e., cross section of a kernel). Current technology offers the potential to consider three-dimensional structural characteristics of grain. X-ray computed tomography (CT) presents one such opportunity to nondestructively extract quantitative data from grain caryopses which can then be related to end-use quality. Results Phenotypic measurements were extracted from CT scans of grain sorghum caryopses. Extensive phenotypic variation was found for embryo volume, endosperm hardness, endosperm texture, endosperm volume, pericarp volume, and kernel volume. CT derived estimates were strongly correlated with ground truth measurements enabling the identification of genotypes with superior structural characteristics. Conclusions Presented herein is a phenotyping pipeline developed to quantify three-dimensional structural characteristics from grain sorghum caryopses which increases the throughput efficiency of previously difficult to measure traits. Adaptation of this workflow to other small-seeded crops is possible providing new and unique opportunities for scientists to study grain in a nondestructive manner which will ultimately lead to improvements end-use quality.

Implementation of genomic prediction can bolster rates of genetic gain in sorghum improvement and permit more efficient allocation of resources within hybrid breeding programs. In the present study, alternative genomic prediction models were compared to assess the potential benefits of including inbred phenotypic records, dominance effects, and genotype-by-environment (G×E) interactions in predicting hybrid grain sorghum performance. Comparisons were made in a set of 395 hybrid combinations derived from 92 parental inbred lines tested in a sparse multienvironment trial. Phenotypic data were collected on hybrids and inbreds for days to mid-anthesis, grain yield, and plant height, and genomic data on parental inbreds were collected by genotyping × sequencing. A significant increase in prediction accuracy was observed when modeling G×E effects; however, dominance effects did not contribute to the overall predictive ability of models in this data set. Including phenotypic data from parental lines significantly improved the prediction of hybrid merit by as much as 17% for days to mid-anthesis, 14% for grain yield, and 33% for plant height when there were no testcross records for a given parental line. Alternatively, similar improvements were not as consistent when the training set included lines already tested in hybrid combinations. Thus, hybrid crop breeders can further optimize genomic predictions for un-testcrossed lines by including non-additive effects and inbred data. INTRODUCTIONGrain sorghum [Sorghum bicolor (L.)] is an ancient cereal grain that is the second most widely grown feed grain in the United States on a production basis (USDA, 2021) and ranks as one of the top 25 major commodities in the world since 1961 (Monk et al., 2015). As a drought-tolerant crop Abbreviations: BLUE, best linear unbiased estimator; BLUP, best linear unbiased predictor; CV, coefficient of variation; GBLUP, genomic best linear unbiased prediction; G×E, genotype-by-environment.

Growth in the niche market of popped grain sorghum [Sorghum bicolor (L.) Moench] has increased the demand for grain sorghum lines or hybrids with improved popping quality. While there is a clear morphological difference in kernel morphology between popcorn [Zea mays L. everta] and most other types of corn, most grain sorghum genotypes have kernels with generally similar morphological structure. The absence of a specific kernel morphology for sorghum makes it impossible to eliminate types of grain sorghum that will not pop based solely on that morphology. Consequently, screening of any sorghum genotype requires the actual popping of grain from that genotype. As such, the identification of traits or combinations thereof that effectively screen grain sorghum genotypes for popping efficiency (PE), expansion ratio (ER), flake size (FS), and popped density (PD) is necessary. Herein, grain from 78 diverse genotypes grown in two environments were characterized for physical (i.e., diameter, thousand kernel weight, kernel hardness index, test weight, and visual hardness rating), compositional (i.e., starch, fiber, fat, ash, and protein), and popping characteristics (i.e., PE, ER, FS, and PD). No single physical or compositional trait was sufficiently correlated to prediction of popping performance. Multi-trait models better predicted popping performance than the single trait correlations. Further, multitrait models using compositional predictors increased prediction accuracies by 10.1% for PE, 42.9% for ER, 24.4% for FS, and 40.6% for PD compared with physical predictors. Among subgroups of genotypes, prediction accuracies varied considerably based on the criteria used to subdivide the genotypes. In conclusion, indirect selection for popping performance is possible by leveraging specific multi-trait models. INTRODUCTIONThe use of sorghum grain [Sorghum bicolor (L.) Moench] in food products has increased in the past ten years because of its neutral flavor, the absence of gluten, and the overall con-

Non-destructive measurements of internal morphological structures in plant materials such as seeds are of high interest in agricultural research. The estimation of pericarp thickness is important to understand the grain quality and storage stability of seeds and can play a crucial role in improving crop yield. In this study, we demonstrate the applicability of fiber-based Bessel beam Fourier domain (FD) optical coherence microscopy (OCM) with a nearly constant high lateral resolution maintained at over ~400 µm for direct non-invasive measurement of the pericarp thickness of two different sorghum genotypes. Whereas measurements based on axial profiles need additional knowledge of the pericarp refractive index, en-face views allow for direct distance measurements. We directly determine pericarp thickness from lateral sections with a 3 µm resolution by taking the width of the signal corresponding to the pericarp at the 1/e threshold. These measurements enable differentiation of the two genotypes with 100% accuracy. We find that trading image resolution for acquisition speed and view size reduces the classification accuracy. Average pericarp thicknesses of 74 µm (thick phenotype) and 43 µm (thin phenotype) are obtained from high-resolution lateral sections, and are in good agreement with previously reported measurements of the same genotypes. Extracting the morphological features of plant seeds using Bessel beam FD-OCM is expected to provide valuable information to the food processing industry and plant breeding programs.