Sorghum genetic, genomic, and breeding resources.

MAIN CONCLUSION: Sorghum research has entered an exciting and fruitful era due to the genetic, genomic, and breeding resources that are now available to researchers and plant breeders. As the world faces the challenges of a rising population and a changing global climate, new agricultural solutions will need to be developed to address the food and fiber needs of the future. To that end, sorghum will be an invaluable crop species as it is a stress-resistant C4 plant that is well adapted for semi-arid and arid regions. Sorghum has already remained as a staple food crop in many parts of Africa and Asia and is critically important for animal feed and niche culinary applications in other regions, such as the United States. In addition, sorghum has begun to be developed into a promising feedstock for forage and bioenergy production. Due to this increasing demand for sorghum and its potential to address these needs, the continuous development of powerful community resources is required. These resources include vast collections of sorghum germplasm, high-quality reference genome sequences, sorghum association panels for genome-wide association studies of traits involved in food and bioenergy production, mutant populations for rapid discovery of causative genes for phenotypes relevant to sorghum improvement, gene expression atlas, and online databases that integrate all resources and provide the sorghum community with tools that can be used in breeding and genomic studies. Used in tandem, these valuable resources will ensure that the rate, quality, and collaborative potential of ongoing sorghum improvement efforts is able to rival that of other major crops.

Transcriptomic and evolutionary analysis of the mechanisms by which P. argentatum, a rubber producing perennial, responds to drought.

BACKGROUND: Guayule (Parthenium argentatum Gray) is a drought tolerant, rubber producing perennial shrub native to northern Mexico and the US Southwest. Hevea brasiliensis, currently the world's only source of natural rubber, is grown as a monoculture, leaving it vulnerable to both biotic and abiotic stressors. Isolation of rubber from guayule occurs by mechanical harvesting of the entire plant. It has been reported that environmental conditions leading up to harvest have a profound impact on rubber yield. The link between rubber biosynthesis and drought, a common environmental condition in guayule's native habitat, is currently unclear. RESULTS: We took a transcriptomic and comparative genomic approach to determine how drought impacts rubber biosynthesis in guayule. We compared transcriptional profiles of stem tissue, the location of guayule rubber biosynthesis, collected from field-grown plants subjected to water-deficit (drought) and well-watered (control) conditions. Plants subjected to the imposed drought conditions displayed an increase in production of transcripts associated with defense responses and water homeostasis, and a decrease in transcripts associated with rubber biosynthesis. An evolutionary and comparative analysis of stress-response transcripts suggests that more anciently duplicated transcripts shared among the Asteraceae, rather than recently derived duplicates, are contributing to the drought response observed in guayule. In addition, we identified several deeply conserved long non-coding RNAs (lncRNAs) containing microRNA binding motifs. One lncRNA in particular, with origins at the base of Asteraceae, may be regulating the vegetative to reproductive transition observed in water-stressed guayule by acting as a miRNA sponge for miR166. CONCLUSIONS: These data represent the first genomic analyses of how guayule responds to drought like conditions in agricultural production settings. We identified an inverse relationship between stress-responsive transcripts and those associated with precursor pathways to rubber biosynthesis suggesting a physiological trade-off between maintaining homeostasis and plant productivity. We also identify a number of regulators of abiotic responses, including transcription factors and lncRNAs, that are strong candidates for future projects aimed at modulating rubber biosynthesis under water-limiting conditions common to guayules' native production environment.

Measurement and Calibration of Plant-Height from Fixed-Wing UAV Images.

Continuing population growth will result in increasing global demand for food and fiber for the foreseeable future. During the growing season, variability in the height of crops provides important information on plant health, growth, and response to environmental effects. This paper indicates the feasibility of using structure from motion (SfM) on images collected from 120 m above ground level (AGL) with a fixed-wing unmanned aerial vehicle (UAV) to estimate sorghum plant height with reasonable accuracy on a relatively large farm field. Correlations between UAV-based estimates and ground truth were strong on all dates (R² > 0.80) but are clearly better on some dates than others. Furthermore, a new method for improving UAV-based plant height estimates with multi-level ground control points (GCPs) was found to lower the root mean square error (RMSE) by about 20%. These results indicate that GCP-based height calibration has a potential for future application where accuracy is particularly important. Lastly, the image blur appeared to have a significant impact on the accuracy of plant height estimation. A strong correlation (R² = 0.85) was observed between image quality and plant height RMSE and the influence of wind was a challenge in obtaining high-quality plant height data. A strong relationship (R² = 0.99) existed between wind speed and image blurriness.

Yield prediction in a peanut breeding program using remote sensing data and machine learning algorithms.

Peanut is a critical food crop worldwide, and the development of high-throughput phenotyping techniques is essential for enhancing the crop's genetic gain rate. Given the obvious challenges of directly estimating peanut yields through remote sensing, an approach that utilizes above-ground phenotypes to estimate underground yield is necessary. To that end, this study leveraged unmanned aerial vehicles (UAVs) for high-throughput phenotyping of surface traits in peanut. Using a diverse set of peanut germplasm planted in 2021 and 2022, UAV flight missions were repeatedly conducted to capture image data that were used to construct high-resolution multitemporal sigmoidal growth curves based on apparent characteristics, such as canopy cover and canopy height. Latent phenotypes extracted from these growth curves and their first derivatives informed the development of advanced machine learning models, specifically random forest and eXtreme Gradient Boosting (XGBoost), to estimate yield in the peanut plots. The random forest model exhibited exceptional predictive accuracy (R2 = 0.93), while XGBoost was also reasonably effective (R2 = 0.88). When using confusion matrices to evaluate the classification abilities of each model, the two models proved valuable in a breeding pipeline, particularly for filtering out underperforming genotypes. In addition, the random forest model excelled in identifying top-performing material while minimizing Type I and Type II errors. Overall, these findings underscore the potential of machine learning models, especially random forests and XGBoost, in predicting peanut yield and improving the efficiency of peanut breeding programs.

Sorghum bicolor x S. halepense interspecific hybridization is influenced by the frequency of 2n gametes in S. bicolor.

Tetraploid johnsongrass [Sorghum halepense (L.) Pers.] is a sexually-compatible weedy relative of diploid sorghum [Sorghum bicolor (L.) Moench]. To determine the extent of interspecific hybridization between male sterile grain sorghum and johnsongrass and the ploidy of their progeny, cytoplasmic (CMS), genetic (GMS) and chemically induced male sterile lines of Tx623 and Tx631 were pollinated with johnsongrass pollen. At maturity 1% and 0.07% of the developing seeds of Tx623 and Tx631 respectively were recovered. Ninety-one percent of recovered hybrids were tetraploid and two percent were triploid, the tetraploids resulting from 2n gametes present in the sorghum female parent. Their formation appears to be genotype dependent as more tetraploids were recovered from Tx623 than Tx631. Because a tetraploid sorghum x johnsongrass hybrid has a balanced genome, they are male and female fertile providing opportunities for gene flow between the two species. Given the differences in 2n gamete formation among Tx623 and Tx631, seed parent selection may be one way of reducing the likelihood of gene flow. These studies were conducted in controlled and optimum conditions; the actual outcrossing rate in natural conditions is expected to be much lower. More studies are needed to assess the rates of hybridization, fitness, and fertility of the progeny under field conditions.

Unmanned Aerial Vehicles for High-Throughput Phenotyping and Agronomic Research.

Advances in automation and data science have led agriculturists to seek real-time, high-quality, high-volume crop data to accelerate crop improvement through breeding and to optimize agronomic practices. Breeders have recently gained massive data-collection capability in genome sequencing of plants. Faster phenotypic trait data collection and analysis relative to genetic data leads to faster and better selections in crop improvement. Furthermore, faster and higher-resolution crop data collection leads to greater capability for scientists and growers to improve precision-agriculture practices on increasingly larger farms; e.g., site-specific application of water and nutrients. Unmanned aerial vehicles (UAVs) have recently gained traction as agricultural data collection systems. Using UAVs for agricultural remote sensing is an innovative technology that differs from traditional remote sensing in more ways than strictly higher-resolution images; it provides many new and unique possibilities, as well as new and unique challenges. Herein we report on processes and lessons learned from year 1-the summer 2015 and winter 2016 growing seasons-of a large multidisciplinary project evaluating UAV images across a range of breeding and agronomic research trials on a large research farm. Included are team and project planning, UAV and sensor selection and integration, and data collection and analysis workflow. The study involved many crops and both breeding plots and agronomic fields. The project's goal was to develop methods for UAVs to collect high-quality, high-volume crop data with fast turnaround time to field scientists. The project included five teams: Administration, Flight Operations, Sensors, Data Management, and Field Research. Four case studies involving multiple crops in breeding and agronomic applications add practical descriptive detail. Lessons learned include critical information on sensors, air vehicles, and configuration parameters for both. As the first and most comprehensive project of its kind to date, these lessons are particularly salient to researchers embarking on agricultural research with UAVs.