Sugar release Supplmentary Table and notes
Sugar release and growth of biofuel crops are improved by downregulation of pectin biosynthesis
bio engineered↗
SEARCH · Engineering Papers
Results for “Switchgrass”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Glycosyl linkage analysis of fractionated cell walls from P.deltoides WT and PdGAUT4-KD lines.
Glycosyl linkage analysis of fractionated cell walls from P.deltoides WT and PdGAUT4-KD lines.
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BLUP and mean yields of selected top 25 high yielding half-sib families
BLUP and mean yields of selected top 25 high yielding half-sib families. The yield BLUP were estimated using mixed linear models. The family column indicates the families with progeny column showing the corresponding progenies to selected families. ‘Wat’ here has been used to denote Watkinsville, ‘Tif’ for Tifton and ‘TN’ for Tennessee.
09 BIOMASS FUELS↗
In Situ Non-Destructive Temporal Measurements of the Rhizosphere Microbiome ‘Hot-Spots’ Using Metaproteomics
Rhizosphere arguably embodies the most diverse microbial ecosystem on the planet, yet it is largely a functional ‘black box’ of belowground plant-microbiome interactions. The rhizosphere is the primary site of entry for subsurface injection of fixed carbon (C) into soil with impacts on local to global scale C biogeochemistry and ultimately Earth’s climate. While spatial organization of rhizosphere is central to its function, small scale and steep microbial and geochemical gradients within this dynamic region make it easily disrupted by sampling. The significant challenge presented by sampling blocks elucidation of discreet functions, drivers, and interactions within rhizosphere ecosystems. Here, we describe a non-destructive sampling method linked to metaproteomic analysis in order to measure temporal shifts in the microbial composition and function of rhizosphere. A robust, non-destructive method of sampling microbial hotspots within rhizosphere provides an unperturbed window into the elusive functional interactome of this system over time and space.
59 BASIC BIOLOGICAL SCIENCES↗
Estimating Field-Level Perennial Bioenergy Grass Biomass Yields Using the Normalized Difference Red-Edge Index and Linear Regression Analysis for Central Virginia, USA
We investigated the indicative power of the normalized difference red-edge index (NDRE) for estimating field-level perennial bioenergy grass biomass yields utilizing Sentinel-2 imagery and a linear regression model as a rapid, cost-effective method for biomass yield estimations for bioenergy. We used 2019 data from three study sites containing mature perennial bioenergy grass stands in central Virginia, USA. Of the simulated daily NDRE values based on the temporally weighted averaging of two temporal neighbors, we found the strongest index–yield correlation on 11 August (R = 0.85). We estimated the perennial bioenergy grass biomass yields for (1) all sites using the data pooled from the three sites (all-site estimation) and (2) each site using the data pooled from the other two sites (cross-site estimation). The estimated field-level perennial bioenergy grass biomass yields strongly correlated with the recorded yields (average R2 = 0.76), with a root mean square error (RMSE) of 1.5 Mg/ha and a mean absolute error (MAE) of 1.2 Mg/ha for the all-site estimation. For the cross-site estimation, the site with diverse perennial grass types had the weakest correlation (R2 = 0.44) of the sites, indicating a difficulty in accounting for heterogeneous index–yield relationships in a single model. In addition to identifying a strong indicative power of the NDRE for estimating the overall perennial bioenergy grass biomass yields at a field level, the findings from this study call for an analysis across multiple perennial grasses and a comparison using multiple sites to understand (1) if the indicative power of the index shifts from the biomass of the specific perennial bioenergy grass type to the overall biomass during the growing season and (2) the level of perennial bioenergy grass heterogeneity that may hinder the remotely sensed biomass yield estimation using a single model.
09 BIOMASS FUELS↗
Remote Sensing-Based Estimation of Advanced Perennial Grass Biomass Yields for Bioenergy
A sustainable bioeconomy would require growing high-yielding bioenergy crops on marginal agricultural areas with minimal inputs. To determine the cost competitiveness and environmental sustainability of such production systems, reliably estimating biomass yield is critical. However, because marginal areas are often small and spread across the landscape, yield estimation using traditional approaches is costly and time-consuming. This paper demonstrates the (1) initial investigation of optical remote sensing for predicting perennial bioenergy grass yields at harvest using a linear regression model with the green normalized difference vegetation index (GNDVI) derived from Sentinel-2 imagery and (2) evaluation of the model’s performance using data from five U.S. Midwest field sites. The linear regression model using midsummer GNDVI predicted yields at harvest with R2 as high as 0.879 and a mean absolute error and root mean squared error as low as 0.539 Mg/ha and 0.616 Mg/ha, respectively, except for the establishment year. Perennial bioenergy grass yields may be predicted 152 days before the harvest date on average, except for the establishment year. The green spectral band showed a greater contribution for predicting yields than the red band, which is indicative of increased chlorophyll content during the early growing season. Although additional testing is warranted, this study showed a great promise for a remote sensing approach for forecasting perennial bioenergy grass yields to support critical economic and logistical decisions of bioeconomy stakeholders.
09 BIOMASS FUELS↗
Bio Blend Remix
Biomass blending is a method of mixing several types of biomass to reduce variability in cost and to improve the sustainability of biofuel productions. Idaho National Laboratory researchers have discovered ways to produce quality, low cost feedstocks year round under a wide variety of environmental conditions.
09 BIOMASS FUELS↗
Recovery Act: Pilot Integrated Cellulosic Biorefinery Operations to Fuel Ethanol
DOE EE002875 Technical Report Public Release. The objective was to leverage ICM’s pre-existing corn to ethanol pilot plant to build and to commission a fully functional pilot integrated cellulosic biorefinery. ICM’s Integrated Biorefinery (IBR) project was designed to achieve four major objectives. These primary goals were achieved during the performance period from December 2009 – August 2015. The design and construction phase took place from December 2009 until August 2011, with an increase in budget of nearly 4 million dollars. This increased cost was offset by operational changes, so the amount spent for the overall project increased by less than $500,000. There were three 1,000-hour performance test conducted, which produced cellulosic feedstock.
09 BIOMASS FUELS↗