Economic impact of yield and composition variation in bioenergy crops: Populus trichocarpa

Abstract

To achieve a bio-based economy, it is necessary to consider variability within a feedstock population. We must understand the range of key phenotypic characteristics when selecting economically advantageous genotypes for domestication in an optimized supply chain. In this analysis we measured cell-wall composition traits in a large natural variant population of Populus trichocarpa. The results were combined with agronomic growth data from the matching genotype to conduct various techno-economic analyses, evaluating the impacts of physical and compositional variability and determining the ultimate phenotypic drivers for yield and economic metrics. Here we show that, although ethanol yield per land area per year and minimum fuel selling price were most strongly impacted by tree size, when considering the largest 25% of trees, size and carbohydrate content were nearly identical influences on minimal fuel selling price, highlighting the need to focus on both size and carbohydrate content in selecting economically optimal feedstocks.

Abundance of Major Cell Wall Components in Natural Variants and Pedigrees of Populus trichocarpa

“…More recently, our study that controlled for technical and micro-spatial error on several controlled crosses in Populus trichocarpa were similar: H 2 was 0.56 for lignin content and 0.81 for the S/G ratio (Harman-Ware et al, 2021). Correlations between C5 and C6 sugars, lignin content, and S/G ratio have been observed in P. trichocarpa (Guerra et al, 2016;Happs et al, 2021); for example, lignin and the S/G ratio displayed a moderate positive correlation (r g = 0.37). Other phenotypes such as enzymatic sugar release (a biomass recalcitrance metric) have also shown correlations with biomass composition phenotypes such as S/G ratio, as demonstrated recently in willow (r p = ∼0.4) (Ohlsson et al, 2019).…”

“…In total, 924 P. trichocarpa natural accessions were grown in OR, United States, and sampled as described previously (Muchero et al, 2015;Chhetri et al, 2019;Happs et al, 2020). In brief, increment cores from 3-year-old trees were debarked, dried, and milled.…”

“…Biomass that had been dried, debarked, milled, and sieved to −20/+80 mesh, ethanol extracted, and destarched was used to determine cell wall sugar composition using high-throughput hydrolysis followed by the NMR analysis of hydrolyzates based on methods described previously (Sluiter et al, 2011;Gjersing et al, 2013;Happs et al, 2021). This method was chosen as it was able to quickly obtain the sugar composition of biomass to build models for sugar prediction by py-MBMS and for the validation of sugar composition estimates.…”

“…Currently, there is a need to utilize rapid techniques capable of analyzing large datasets to determine the sugar composition derived from cellulose and hemicelluloses in biomass in an effort to inform systems biology models, to develop sustainable and consistent feedstocks, and to inform field-to-fuel insights to track changes in biomass composition. The high-throughput analysis of cell wall sugars in lignocellulosic biomass is difficult to achieve as typical methodologies require many steps, including hydrolysis, prior to the analysis of released sugars by high-performance liquid chromatography (HPLC) or nuclear magnetic resonance (NMR; Sluiter et al, 2011;Happs et al, 2020). Various types of high-throughput methods have been developed to estimate sugar composition in biomass and typically involve the use of hydrolysis steps, robotics, and plate reading technology (Decker et al, 2018).…”

“…Various types of high-throughput methods have been developed to estimate sugar composition in biomass and typically involve the use of hydrolysis steps, robotics, and plate reading technology (Decker et al, 2018). Gjersing et al (2013) and Happs et al (2021) have developed high-throughput methods for the determination of sugar content in biomass by means of hydrolysis followed by the analysis of hydrolyzates using NMR. The NMR analysis of biomass hydrolyzates is capable of estimating the composition of major and minor sugars present in lignocellulosic biomass cell walls but is still limited in throughput by laborious hydrolysis steps prior to the rapid analysis of the products on the spectrometer.…”