Maria Guadalupe Salas‐Fernandez

With the global population explosion, the increasing demand in food supply pushes the development of advanced breeding methods. This study presents a bi-stage data-driven and process-based crop model to provide breeding recommendations based on Genotype x Environment (GxE) effects for sorghum, a vital cereal crop with various plant types, such as Grain (G), Forage (F), Dual Purpose (DP), and Photoperiod-Sensitive (PS). The model combines traditional process-based crop modeling with explainable data-driven methods, which increases the general interpretability and flexibility of the model. The model considers extensive environmental data, including seven years of hourly weather records and soil factors from three research farms in Iowa, together with management practices and parental information from 651 males and 131 females. Additionally, the model predicts the hourly dry weight of sorghum’s leaves, stems and grain, and predicts final yield based on management practices. The final combined Relative Root mean squared error reached 16% to 19% across several environmental conditions, which demonstrating the robust predictive capabilities. Besides, the model effectively identified elite hybrids in four distinct sorghum types, which also demonstrated its utility in reducing the need for extensive field trials. Additionally, our analysis of genotype by environment interactions revealed significant variability in performance, which indicates the precise breeding strategies customized for the environmental conditions are important and vital. This research highlights that our explainable hybrid model framework can greatly improve crop modeling and plant breeding, making agriculture more efficient and sustainable.

Accurate simulation of crop growth processes for predicting final yield is critical for optimizing resource management, particularly in regions with variable climates and limited resource availability. This paper proposes a novel data-driven crop model to simulate phenotypic changes during biomass sorghum growth. The model integrates a detailed physiological framework for sorghum development—tracking how phenotypes are determined by genotype, environment, management practices, and their interactions—with data-driven techniques to calibrate genotypic parameters using experimental data. Results demonstrate that the model achieves accurate biomass production predictions and successfully disentangles the effects of environmental and management factors on phenotypic development, even with limited data. This model enhances the accuracy and applicability of biomass sorghum growth and yield prediction models, offering valuable insights for precision agriculture.

Crop growth rate is a critical physiological trait for forage and bioenergy crops like sorghum [ Sorghum bicolor (L.) Moench], influencing overall crop productivity, particularly in photoperiod‐sensitive (PS) types. Crop growth rate studies focus on either a physiological approach utilizing a few genotypes to analyze biomass accumulation or a genetic approach characterizing easily scorable proxy traits in larger populations. Thus, the genetic control of crop growth rate in terms of biomass accumulation is poorly understood in PS sorghum. In this study, we monitored biomass accumulation in a diverse panel comprising 269 PS sorghum accessions in two growing seasons. We performed sequential samplings at 11 timepoints, separating leaves from stems. For the total biomass and each fraction, we applied the beta growth function to determine the maximum crop growth rate ( c _m ), maximum biomass accumulation ( w _max ), and time to c _m ( t _m ). Significant genetic variability was observed for all three parameters. Our analysis identified a practical window for c _m assessment through accumulated biomass at 60–70 days after planting. Genome‐wide association analysis suggested distinct and independent genetic controls of leaf and stem biomass accumulation, both physically and temporally. Common genomic regions were discovered controlling w _max and c _m of stem and total biomass. These results provide new insights into the genetic control of crop growth rate, highlighting promising genomic regions for functional validation. This research also offers practical applications for plant breeding programs demonstrating the feasibility of selecting superior genotypes for both early and late biomass accumulation to enhance crop productivity.

Canopy architecture traits are associated with productivity in sorghum [Sorghum bicolor (L.) Moench], and they are commonly measured at the time of flowering or harvest. Little is known about the dynamics of canopy architecture traits through the growing season. Utilizing the ground‐based high‐throughput phenotyping system Phenobot 1.0, we collected stereo images of a photoperiod‐sensitive and a photoperiod‐insensitive population over time to generate three‐dimensional (3D) representations of the canopy. Four descriptors were automatically extracted from the 3D point clouds: plot‐based plant height (PBPH), plot‐based plant width (PBPW), plant surface area (PSA), and convex hull volume (CHV). Additionally, genotypic growth rates were estimated for each canopy descriptor. Genome‐wide association analysis was performed on individual timepoints and the growth rates in both populations. We detected genotypic variation for each of the four canopy descriptors and their growth rates and discovered novel genomic regions associated with growth rates on chromosomes 1 (PBPH, CHV), 3 (PBPH), 4 (PBPH, PBPW), 5 (PBPH), 8 (PSA), and 9 (PBPW). These results provide new knowledge about the genetic control of canopy architecture, highlighting genomic regions that can be targeted in plant breeding programs.

Background: Leaf angle is an important plant architecture trait, affecting plant density, light interception efficiency, photosynthetic rate, and yield. The “smart canopy” model proposes more vertical leaves in the top plant layers and more horizontal leaves in the lower canopy, maximizing conversion efficiency and photosynthesis. Sorghum leaf arrangement is opposite to that proposed in the “smart canopy” model, indicating the need for improvement. Although leaf angle quantitative trait loci (QTL) have been previously reported, only theDwarf3(Dw3) auxin transporter gene, colocalizing with a major-effect QTL on chromosome 7, has been validated. Additionally, the genetic architecture of leaf angle across canopy layers remains to be elucidated.Results: This study characterized the canopy-layer specific transcriptome of five sorghum genotypes using RNA sequencing. A set of 284 differentially expressed genes for at least one layer comparison (FDR < 0.05) co-localized with 69 leaf angle QTL and were consistently identified across genotypes. These genes are involved in transmembrane transport, hormone regulation, oxidation-reduction process, response to stimuli, lipid metabolism, and photosynthesis. The most relevant eleven candidate genes for layer-specific angle modification include those homologous to genes controlling leaf angle in rice and maize or genes associated with cell size/expansion, shape, and cell number.Conclusions: Considering the predicted functions of candidate genes, their potential undesirable pleiotropic effects should be further investigated across tissues and developmental stages. Future validation of proposed candidates and exploitation through genetic engineering or gene editing strategies targeted to collar cells will bring researchers closer to the realization of a “smart canopy” sorghum.

The sorghum [Sorghum bicolor (L.) Moench] parental inbred lines IA100RPS (Reg. no. PL‐319, PI 698107) and IA101RPS (Reg. no. PL‐320, PI 698108) were developed by the Iowa State University sorghum breeding program in Boone, IA, and released in 2019. These restorer inbred lines were generated to provide the forage and biofuel industry with improved germplasm capable of producing photoperiod‐sensitive hybrids with high biomass yield adapted to northern latitudes. IA100RPS and IA101RPS were developed using the pedigree breeding method from the cross of P89008 and RTx2910. IA100RPS is a purple plant with red grain, ∼2.4 m tall, that flowers on average 70 d after planting. IA101RPS is a tan plant with red grain, ∼2.1 m tall, that flowers on average 72 d after planting. From 2015 to 2018, both lines were evaluated in testcross combinations with a set of public A‐lines in multiple locations in the state of Iowa. Hybrids generated with IA100RPS and IA101RPS had significantly higher biomass yield than those derived from RTx2910 and RTx2909, with superior lodging resistance and higher dry matter content. This germplasm adapted to northern latitudes represents novel inbred lines with good general combining ability for the production of photoperiod‐sensitive forage or biofuel hybrids and could be used as parental sources for the development of improved sorghum lines with adaptation to the midwestern United States.

Drought stress causes crop yield losses worldwide. Sorghum is a C₄ species tolerant to moderate drought stress, and its extensive natural variation for photosynthetic traits under water-limiting conditions can be exploited for developing cultivars with enhanced stress tolerance. The objective of this study was to discover genes/genomic regions that control the sorghum photosynthetic capacity under pre-anthesis water-limiting conditions. We performed a genome-wide association study for seven photosynthetic gas exchange and chlorophyll fluorescence traits during three periods of contrasting soil volumetric water content (VWC): control (30% VWC), drought (15% VWC), and recovery (30% VWC). Water stress was imposed with an automated irrigation system that generated a controlled dry-down period for all plants, to perform an unbiased genotypic comparison. A total of 60 genomic regions were associated with natural variation in one or more photosynthetic traits in a particular treatment or with derived variables. We identified 33 promising candidate genes with predicted functions related to stress signaling, oxidative stress protection, hormonal response to stress, and dehydration protection. Our results provide new knowledge about the natural variation and genetic control of sorghum photosynthetic response to drought with the ultimate goal of improving its adaptation and productivity under water stress scenarios.

Community association populations are composed of phenotypically and genetically diverse accessions. Once these populations are genotyped, the resulting marker data can be reused by different groups investigating the genetic basis of different traits. Because the same genotypes are observed and scored for a wide range of traits in different environments, these populations represent a unique resource to investigate both pleiotropy and genotype by environment interactions. Here we assembled a set of 234 separate trait datasets for the Sorghum Association Panel, a group of 406 sorghum genotypes widely employed by the sorghum genetics community. Comparison of genome wide association studies conducted with two independently generated marker sets for this population demonstrate that existing genetic marker sets do not saturate the genome and likely capture only 35-43% of potentially detectable loci controlling variation for traits scored in this population. While limited evidence for pleiotropy was apparent in cross-GWAS comparisons, a multivariate adaptive shrinkage approach recovered both known pleiotropic effects of existing loci and new pleiotropic effects, particularly significant impacts of known dwarfing genes on root architecture. In addition, we identified new loci with pleiotropic effects consistent with known trade-offs in sorghum development. These results demonstrate the potential for mining existing trait datasets from widely used community association populations to enable new discoveries from existing trait datasets as new, denser genetic marker datasets are generated for existing community association populations.

Leaf angle across the sorghum canopy is genetically controlled by both common and layer-specific loci, which holds potential for the development of an optimized

Because structural variation in the inflorescence architecture of cereal crops can influence yield, it is of interest to identify the genes responsible for this variation. However, the manual collection of inflorescence phenotypes can be time consuming for the large populations needed to conduct genome-wide association studies (GWAS) and is difficult for multidimensional traits such as volume. A semiautomated phenotyping pipeline, TIM (Toolkit for Inflorescence Measurement), was developed and used to extract unidimensional and multidimensional features from images of 1,064 sorghum (Sorghum bicolor) panicles from 272 genotypes comprising a subset of the Sorghum Association Panel. GWAS detected 35 unique single-nucleotide polymorphisms associated with variation in inflorescence architecture. The accuracy of the TIM pipeline is supported by the fact that several of these trait-associated single-nucleotide polymorphisms (TASs) are located within chromosomal regions associated with similar traits in previously published quantitative trait locus and GWAS analyses of sorghum. Additionally, sorghum homologs of maize (Zea mays) and rice (Oryza sativa) genes known to affect inflorescence architecture are enriched in the vicinities of TASs. Finally, our TASs are enriched within genomic regions that exhibit high levels of divergence between converted tropical lines and cultivars, consistent with the hypothesis that these chromosomal intervals were targets of selection during modern breeding.