Correlations among morphological and biochemical traits in high-cannabidiol hemp (Cannabis sativa L.).

Cannabis sativa is cultivated for multiple uses including the production of cannabinoids. In developing improved production systems for high-cannabinoid cultivars, scientists and cultivators must consider the optimization of complex and interacting sets of morphological, phenological, and biochemical traits, which have historically been shaped by natural and anthropogenic selection. Determining factors that modulate cannabinoid variation within and among genotypes is fundamental to developing efficient production systems and understanding the ecological significance of cannabinoids. Thirty-two high-cannabinoid hemp cultivars were characterized for traits including flowering date and shoot-tip cannabinoid concentration. Additionally, a set of plant architecture traits, as well as wet, dry, and stripped inflorescence biomass were measured at harvest. One plant per plot was partitioned post-harvest to quantify intra-plant variation in inflorescence biomass production and cannabinoid concentration. Some cultivars showed intra-plant variation in cannabinoid concentration, while many had a consistent concentration regardless of canopy position. There was both intra- and inter-cultivar variation in architecture that correlated with intra-plant distribution of inflorescence biomass, and concentration of cannabinoids sampled from various positions within a plant. These relationships among morphological and biochemical traits will inform future decisions by cultivators, regulators, and plant breeders.

Genome-Wide Associations with Resistance to Bipolaris Leaf Spot (Bipolaris oryzae (Breda de Haan) Shoemaker) in a Northern Switchgrass Population (Panicum virgatum L.).

Switchgrass (Panicum virgatum L.), a northern native perennial grass, suffers from yield reduction from Bipolaris leaf spot caused by Bipolaris oryzae (Breda de Haan) Shoemaker. This study aimed to determine the resistant populations via multiple phenotyping approaches and identify potential resistance genes from genome-wide association studies (GWAS) in the switchgrass northern association panel. The disease resistance was evaluated from both natural (field evaluations in Ithaca, New York and Phillipsburg, Philadelphia) and artificial inoculations (detached leaf and leaf disk assays). The most resistant populations based on a combination of three phenotyping approaches-detached leaf, leaf disk, and mean from two locations-were 'SW788', 'SW806', 'SW802', 'SW793', 'SW781', 'SW797', 'SW798', 'SW803', 'SW795', 'SW805'. The GWAS from the association panel showed 27 significant SNPs on 12 chromosomes: 1K, 2K, 2N, 3K, 3N, 4N, 5K, 5N, 6N, 7K, 7N, and 9N. These markers accumulatively explained the phenotypic variance of the resistance ranging from 3.28 to 26.52%. Within linkage disequilibrium of 20 kb, these SNP markers linked with the potential resistance genes included the genes encoding for NBS-LRR, PPR, cell-wall related proteins, homeostatic proteins, anti-apoptotic proteins, and ABC transporter.

Seeking Alfalfa Resistance to a Rhizophagous Pest, the Clover Root Curculio (Sitona hispidulus F.).

Since the cancellation of broad-spectrum soil-active insecticides in alfalfa (Medicago sativa L.) production, clover root curculio (Sitona hispidulus F.) (CRC) larval root damage has increased. Current CRC management practices are limited in their ability to suppress larval feeding belowground. First, we field screened developmental alfalfa populations for CRC damage. Subsequently, we developed a soil-less arena to observe nodule feeding and development (head capsule width) of larvae in the lab. This method was used to evaluate five alfalfa populations (two CRC-susceptible (control) and three CRC-resistant populations) against larvae. Further, one CRC-resistant population paired with its genetically similar susceptible population were tested against adult leaf consumption and oviposition in the greenhouse. Field screening revealed that the alfalfa populations selected for little or no larval root feeding damage were more resistant to CRC larval feeding than their corresponding unselected cultivars and significantly more resistant than populations selected for susceptibility. The development of a soil-less arena provided a useful method for evaluation of root-larva interactions. Although larval development was similar across susceptible and resistant alfalfa populations, one CRC-resistant population (NY1713) displayed overall increased nodulation and, thus, had a significantly lower proportion of nodules consumed by larvae. Adult feeding and oviposition aboveground were similar across all populations tested. These results provide possible candidates and screening method for the development and evaluation of alfalfa cultivars that may reduce the impacts of larval feeding and that offer an additional option for CRC management.

Genomic Prediction of Biomass Yield in Two Selection Cycles of a Tetraploid Alfalfa Breeding Population.

Alfalfa (Medicago sativa L.) is a widely planted perennial forage legume grown throughout temperate and dry subtropical regions in the world. Long breeding cycles limit genetic improvement of alfalfa, particularly for complex traits such as biomass yield. Genomic selection (GS), based on predicted breeding values obtained using genome-wide molecular markers, could enhance breeding efficiency in terms of gain per unit time and cost. In this study, we genotyped tetraploid alfalfa plants that had previously been evaluated for yield during two cycles of phenotypic selection using genotyping-by-sequencing (GBS). We then developed prediction equations using yield data from three locations. Approximately 10,000 single nucleotide polymorphism (SNP) markers were used for GS modeling. The genomic prediction accuracy of total biomass yield ranged from 0.34 to 0.51 for the Cycle 0 population and from 0.21 to 0.66 for the Cycle 1 population, depending on the location. The GS model developed using Cycle 0 as the training population in predicting total biomass yield in Cycle 1 resulted in accuracies up to 0.40. Both genotype × environment interaction and the number of harvests and years used to generate yield phenotypes had effects on prediction accuracy across generations and locations, Based on our results, the selection efficiency per unit time for GS is higher than phenotypic selection, although accuracies will likely decline across multiple selection cycles. This study provided evidence that GS can accelerate genetic gain in alfalfa for biomass yield.