Evaluation of UAV-derived multimodal remote sensing data for biomass prediction and drought tolerance assessment in bioenergy sorghum

Screening for drought tolerance is critical to ensure high biomass production of bioenergy sorghum in arid or semi-arid environments. The bottleneck in drought tolerance selection is the challenge of accurately predicting biomass for a large number of genotypes. Although biomass prediction by lowaltitude remote sensing has been widely investigated on various crops, the performance of the predictions are not consistent, especially when applied in a breeding context with hundreds of genotypes. In some cases, biomass prediction of a large group of genotypes benefited from multimodal remote sensing data;while in other cases, the benefits were not obvious. In this study, we evaluated the performance of single and multimodal data(thermal, RGB, and multispectral) derived from an unmanned aerial vehicle(UAV) for biomass prediction for drought tolerance assessments within a context of bioenergy sorghum breeding. The biomass of 360 sorghum genotypes grown under well-watered and water-stressed regimes was predicted with a series of UAV-derived canopy features, including canopy structure, spectral reflectance, and thermal radiation features. Biomass predictions using canopy features derived from the multimodal data showed comparable performance with the best results obtained with the single modal data with coefficients of determination(R2) ranging from 0.40 to 0.53 under water-stressed environment and0.11 to 0.35 under well-watered environment. The significance in biomass prediction was highest with multispectral followed by RGB and lowest with the thermal sensor. Finally, two well-recognized yieldbased drought tolerance indices were calculated from ground truth biomass data and UAV predicted biomass, respectively. Results showed that the geometric mean productivity index outperformed the yield stability index in terms of the potential for reliable predictions by the remotely sensed data.Collectively, this study demonstrated a promising strategy for the use of different UAV-based imaging sensors to quantify yield-based drought tolerance.

Influence of harvest date on seed yield and quality in forage kochia

Forage kochia(Bassia prostrata) is used for rangeland reclamation and livestock and wildlife forage,but limited research has been conducted on its seed production. Therefore, this research evaluated the effect of harvest date on seed weight, germination, and seed yield of forage kochia subspecies virescens and grisea. Seed was harvested from individual plants for 3 years during October, November, and December. October harvest had the lightest 100-seed weights, with the November harvest slightly heavier than December, for most accessions.Cultivar Snowstorm and breeding line Sahsel, both subsp. grisea, had the greatest 100-seed weights in November, 155 and 143 mg, respectively, whereas, cv.Immigrant(subsp. virescens), the standard for forage kochia, ranked among the least for 100-seed weight. For most accessions, germination was lowest from the October harvest(11%–43%), with greater germination with November and December harvested seeds(43%–64%).Viable seed yields were greatest in November with the exception of two accessions, which peaked in October,indicating earlier maturity. Results indicate that forage kochia usually reaches optimum seed maturity by early November, after plants are exposed to freezing temperatures; however, earlier maturing accessions exist in both subspecies virescens and grisea.