Replacing fossil fuels with cellulosic biofuels is a valuable component of reducing the drivers of climate change. This leads to a requirement to develop more productive bioenergy crops, such as Arundo donax with the aim of increasing above-ground biomass (AGB). However, direct measurement of AGB is time consuming, destructive, and labor-intensive. Phenotyping of plant height and biomass production is a bottleneck in genomics- and phenomics-assisted breeding. Here, an unmanned aerial vehicle (UAV) for remote sensing equipped with light detection and ranging (LiDAR) was tested for remote plant height and biomass determination in A. donax. Experiments were conducted on three A. donax ecotypes grown in well-watered and moderate drought stress conditions. A novel UAV-LiDAR data collection and processing workflow produced a dense three-dimensional (3D) point cloud for crop height estimation through a normalized digital surface model (DSM) that acts as a crop height model (CHM). Manual measurements of crop height and biomass were taken in parallel and compared to LiDAR CHM estimates. Stepwise multiple regression was used to estimate biomass. Analysis of variance (ANOVA) tests and pairwise comparisons were used to determine differences between ecotypes and drought stress treatments. We found a significant relationship between the sensor readings and manually measured crop height and biomass, with determination coefficients of 0.73 and 0.71 for height and biomass, respectively. Differences in crop heights were detected more precisely from LiDAR estimates than from manual measurement. Crop biomass differences were also more evident in LiDAR estimates, suggesting differences in ecotypes’ productivity and tolerance to drought. Based on these results, application of the presented UAV-LiDAR workflow will provide new opportunities in assessing bioenergy crop morpho-physiological traits and in delivering improved genotypes for biorefining.

Increasing number of metagenome sequencing studies have proposed a central metabolic role of still understudied Archaeal members in natural and artificial ecosystems. However, their role in hydrocarbon cycling, particularly in the anaerobic biodegradation of aliphatic and aromatic hydrocarbons, is still mostly unknown in both marine and terrestrial environments. In this work, we focused our study on the metagenomic characterization of the archaeal community inhabiting the Mar Piccolo (Taranto, Italy, central Mediterranean) sediments heavily contaminated by petroleum hydrocarbons and polychlorinated biphenyls (PCB). Among metagenomic bins reconstructed from Mar Piccolo microbial community, we have identified members of the Asgardarchaeota superphylum that has been recently proposed to play a central role in hydrocarbon cycling in natural ecosystems under anoxic conditions. In particular, we found members affiliated with Thorarchaeota, Heimdallarchaeota, and Lokiarchaeota phyla and analyzed their genomic potential involved in central metabolism and hydrocarbon biodegradation. Metabolic prediction based on metagenomic analysis identified the malonyl-CoA and benzoyl-CoA routes as the pathways involved in aliphatic and aromatic biodegradation in these Asgardarchaeota members. This is the first study to give insight into the archaeal community functionality and connection to hydrocarbon degradation in marine sediment historically contaminated by hydrocarbons.