Cannabinoids function in defense against chewing herbivores in Cannabis sativa L.

In the decades since the first cannabinoids were identified by scientists, research has focused almost exclusively on the function and capacity of cannabinoids as medicines and intoxicants for humans and other vertebrates. Very little is known about the adaptive value of cannabinoid production, though several hypotheses have been proposed including protection from ultraviolet radiation, pathogens, and herbivores. To test the prediction that genotypes with greater concentrations of cannabinoids will have reduced herbivory, a segregating F2 population of Cannabis sativa was leveraged to conduct lab- and field-based bioassays investigating the function of cannabinoids in mediating interactions with chewing herbivores. In the field, foliar cannabinoid concentration was inversely correlated with chewing herbivore damage. On detached leaves, Trichoplusia ni larvae consumed less leaf area and grew less when feeding on leaves with greater concentrations of cannabinoids. Scanning electron and light microscopy were used to characterize variation in glandular trichome morphology. Cannabinoid-free genotypes had trichomes that appeared collapsed. To isolate cannabinoids from confounding factors, artificial insect diet was amended with cannabinoids in a range of physiologically relevant concentrations. Larvae grew less and had lower rates of survival as cannabinoid concentration increased. These results support the hypothesis that cannabinoids function in defense against chewing herbivores.

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.

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.