RECYCLING BIOSOLIDS TO IMPROVE MARGINAL LANDS FOR BIOENERGY FEEDSTOCK PRODUCTION IN UKRAINE

Energy independence is one of the national priorities facing Ukraine today. Plant-based feedstocks have the potential to diversify Ukraine’s energy independence by decreasing dependence on petroleum-based energy, reducing greenhouse gas emissions, expanding renewable fuel industries and creating job opportunities. However, biofeedstock needs to be competitive on availability, performance, and price to produce, market, and produce fuels. We hypothesize that domestically produced feedstocks from sweet sorghum, using proactive recycling of nutrient-rich biosolids on vast areas of degraded and marginal lands, could be a win-win energy independence strategy in Ukraine. Our goal is to create for generating a steady-state source of biofeedstock and disseminate science-based knowledge and training to the clientele. Specific objectives are to: (1) establish research studies to evaluate growth and feedstock productivity, nutrient removal, and feedstock characteristics of sweet sorghum fertilized with biosolids on degraded and marginal lands in Rivne, Kherson, Dnipro, and Kyiv regions of Ukraine; and (2) determine the impact of biosolids and sweet sorghum on soil quality. Data collected on growth, feedstock production, feedstock characteristics, fuel potential, and high-value co-products (biochar) of sweet sorghum and soil quality will be evaluated by multivariate statistics. Input, output, and outreach data will be subject to techno-economic analyses to evaluate the economically viability, environmentally compatibility, and social acceptability of the project. Traditional and electronic outlet activities will be utilized to disseminate outcomes and outputs and to evaluate project impacts.

Sustainability Trait Modeling of Field-Grown Switchgrass (Panicum virgatum) Using UAV-Based Imagery

Unmanned aerial vehicles (UAVs) provide an intermediate scale of spatial and spectral data collection that yields increased accuracy and consistency in data collection for morphological and physiological traits than satellites and expanded flexibility and high-throughput compared to ground-based data collection. In this study, we used UAV-based remote sensing for automated phenotyping of field-grown switchgrass (Panicum virgatum), a leading bioenergy feedstock. Using vegetation indices calculated from a UAV-based multispectral camera, statistical models were developed for rust disease caused by Puccinia novopanici, leaf chlorophyll, nitrogen, and lignin contents. For the first time, UAV remote sensing technology was used to explore the potentials for multiple traits associated with sustainable production of switchgrass, and one statistical model was developed for each individual trait based on the statistical correlation between vegetation indices and the corresponding trait. Also, for the first time, lignin content was estimated in switchgrass shoots via UAV-based multispectral image analysis and statistical analysis. The UAV-based models were verified by ground-truthing via correlation analysis between the traits measured manually on the ground-based with UAV-based data. The normalized difference red edge (NDRE) vegetation index outperformed the normalized difference vegetation index (NDVI) for rust disease and nitrogen content, while NDVI performed better than NDRE for chlorophyll and lignin content. Overall, linear models were sufficient for rust disease and chlorophyll analysis, but for nitrogen and lignin contents, nonlinear models achieved better results. As the first comprehensive study to model switchgrass sustainability traits from UAV-based remote sensing, these results suggest that this methodology can be utilized for switchgrass high-throughput phenotyping in the field.

Multivariate Analysis of Seed Chemical Diversity among Jatropha curcas in Botswana

Jatropha (Jatropha curcas L.) has been identified as a potential bioenergy feedstock in arid regions, but knowledge of the diversity of its chemical characteristics is limited. In this study, 61 Jatropha accessions growing in Botswana, where both severe drought and winter frosts frequently occur, were analyzed for their seed chemical properties. Histogram analyses and meta-analysis comparisons with seeds from other countries/continents showed that the median/mean dry seed weight, toxic compound phorbol esters, and C18:0 fatty acid levels in the Botswanan accessions were lower than those from other countries/continents. A clustered heat map analysis indicated five clades for the Botswanan accessions, and their physicochemical traits were also categorized into five groups. Many positive and negative correlations were observed among the chemical traits, including negative correlations between the C18:3 (linolenic acid) content and yield-related traits (lipid content and dry seed weight). Principal component analysis highlighted the existence of accessions with highly deviated seed chemical compositions, such as those enriched in C18:0/C18:1 and C16:0/C16:1/C18:3 fatty acids. Overall, the present study suggests considerable diversity in the seed chemical compositions of Botswanan Jatropha accessions. Various accessions could be useful as feedstock for specific industrial products, as well as for breeding materials for the fortification of specific chemical ingredients.

Identification of genetic markers and wood properties that predict wood biorefinery potential in aspen bioenergy feedstock (Populus tremula).

Wood represents the majority of the biomass on lands, and it constitutes a renewable source of biofuels and other bioproducts. However, wood is recalcitrant to bioconversion, meaning that feedstocks must be improved. We investigated the properties of wood that affect bioconversion, as well as the underlying genetics, to help identify superior biorefinery tree feedstocks. We recorded as many as 65 wood-related and growth traits in a population of European aspen natural genotypes. These traits included three growth and field performance traits, 20 traits for wood chemical composition, 17 traits for wood anatomy and structure, and 25 wood saccharification traits as indicators of bioconversion potential. We used statistical modelling to determine which wood traits best predict bioconversion yield traits. This way, we identified a core set of wood properties that predict bioprocessing traits. Several of these predictor traits showed high broad-sense heritability, suggesting potential for genetic improvement of feedstocks. Finally, we performed genome-wide association study (GWAS) to identify genetic markers for yield traits or for wood traits that predict yield. GWAS revealed only a few genetic markers for saccharification yield traits, but many more SNPs were associated with wood chemical composition traits, including predictors traits for saccharification. Among them, 16 genetic markers associated specifically with lignin chemical composition were situated in and around two genes which had not previously been associated with lignin. Our approach allowed linking aspen wood bioprocessing yield to wood properties and the underlying genetics, including the discovery of two new potential regulator genes for wood chemical composition.