Perennial grasses are touted as sustainable feedstocks for energy production. Such benefits, however, may be offset if excessive nitrogen (N) fertilization leads to economic and environmental issues. Furthermore, as yields respond to changes in climate, nutrient requirements will change, and thus guidance on minimal N inputs is necessary to ensure sustainable bioenergy production. Here, a pairwise meta-analysis was conducted to investigate the effects of N fertilization (amount and duration) and climate on the above-ground biomass yields of miscanthus (Miscanthus x giganteus) and switchgrass (Panicum virgatum L.). Both regression models and meta-analyses showed that switchgrass was more responsive to N than miscanthus, although both showed significant and positive N effects. Meta-analysis further showed that the positive growth response of miscanthus to N application increased with N addition rates of 60–300 kg N ha−1 year−1, but the magnitude of the response decreased with the number of years of fertilization (duration). N effects on switchgrass biomass increased and peaked at rates of 120–160 kg N ha−1 year−1 and 5–6 years of N inputs, but diminished for rates >300 kg N ha−1 year−1 and >7 years. Meta-analysis further revealed that the influences of N on switchgrass increased with both mean annual temperature and precipitation. Miscanthus yields were less responsive to climate than switchgrass yields. This meta-analysis helps fill a gap in estimation of biofeedstock yields based on N fertilization and could help better estimate minimum N requirements and soil management strategies for miscanthus and switchgrass cultivation across climatic conditions, thereby improving the efficiency and sustainability of bioenergy cropping systems.
Practicing agriculture decreases downstream water quality when compared to non-agricultural lands. Agricultural watersheds that also grow perennial biofuel feedstocks can be designed to improve water quality compared to agricultural watersheds without perennials. The question then becomes which conservation practices should be employed and where in the landscape should they be situated to achieve water quality objectives when growing biofuel feedstocks. In this review, we focused on four types of spatial decisions in a bioenergy landscape: decisions about placement of vegetated strips, artificial drainage, wetlands, and residue removal. The appropriate tools for addressing spatial design questions are optimizations that seek to minimize losses of sediment and nutrients, reduce water temperature, and maximize farmer income. To accomplish these objectives through placing conservation practices, both field-scale and watershed-scale cost and benefits should be considered, as many biophysical processes are scale dependent. We developed decision trees that consider water quality objectives and landscape characteristics when determining the optimal locations of management practices. These decision trees summarize various rules for placing practices and can be used by farmers and others growing biofuels. Additionally, we examined interactions between conservation practices applied to bioenergy landscapes to highlight synergistic effects and to comprehensively address the question of conservation practice usage and placement. We found that combining conservation practices and accounting for their interactive effects can significantly improve water quality outcomes. Based on our review, we determine that by making spatial decisions on conservation practices, bioenergy landscapes can be designed to improve water quality and enhance other ecosystem services.
This data article focuses on sustainability indicators for bioenergy generation from Brazilian Amazon׳s non-woody native biomass sources, considered to be modern forms of biomass. In the construction of the indicators, the Indicator-based Framework for Evaluation of Natural Resource Management Systems (MESMIS, from the original Spanish) method was used, with the application of the seven sustainability attributes to identify critical points and limiting and favorable factors for sustainability. The data yielded a list of 29 indicators distributed across 27 critical points, selected from three system evaluation areas: 11 environmental indicators, 11 social indicators, and 7 economic indicators.
The economic potential for Eucalyptus spp. production for jet fuel additives in the United States: A 20 year projection suite of scenarios ranging from $110 Mg-1 to $220 Mg-1 utilizing the POLYSYS model.
Link to the website with documentation and download instructions for the PNNL Global Change Assessment Model (GCAM), a community model or long-term, global energy, agriculture, land use, and emissions. BioEnergy production, transformation, and use is an integral part of GCAM modeling and scenarios.
The goal of this repository is to promote transparency and ease-of-access to the U.S. Department of Energy Bioenergy Technologies Office (BETO) supported public studies involving techno-economic analysis (TEA). As such, this database summarizes the economic and technical parameters associated with the modeled biorefinery processes for the production of biofuels and bioproducts, as presented in a range of published reports and papers. The database serves as a quick reference tool by documenting and referencing the results of techno-economic analyses from the national laboratories and in peer-reviewed journals.
The analyses presented in this database may be distinguished in several regards, such as cost year, feedstock cost, and financial assumptions (tax rate, percent equity, project lifetime, etc.), and reflect details as they were provided in the original studies. Accordingly, the intent of this database is not to directly compare one technology pathway against another, and caution should be taken in interpreting the outputs as such.
This work was authored by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Bioenergy Technologies Office. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes.
Advanced biomass feedstocks tend to provide more non-fuel ecosystem goods and services (ES) than 1st-generation alternatives. We explore the idea that payment for non-fuel ES could facilitate market penetration of advanced biofuels by closing the profitability gap. As a specific example, we discuss the Mississippi-Atchafalaya River Basin (MARB), where 1st-generation bioenergy feedstocks (e.g., corn-grain) have been integrated into the agricultural landscape. Downstream, the MARB drains to the Gulf of Mexico, where the most-valuable fishery in the US is impacted by annual formation of a large hypoxic "Dead zone." We suggest that advanced biomass production systems in the MARB can increase and stabilize the provision of ES derived from the coastal and marine ecosystems of the Gulf-of-Mexico. Upstream, we suggest that choosing feedstocks based on their resistance or resilience to disturbance (e.g., perennials, diverse feedstocks) can increase reliability in ES provision over time. Direct feedbacks to incentivize producers of advanced feedstocks are currently lacking. Perhaps a shift from first-generation biofuels to perennial-based fuels and other advanced bioenergy systems (e.g., algal diesel, biogas from animal wastes) can be encouraged by bringing downstream environmental externalities into the market for upstream producers. In future, we can create such feedbacks through payments for ES, but significant research is needed to pave the way.
Reducing dependence on fossil‐based energy has raised interest in biofuels as a potential energy source, but concerns have been raised about potential implications for water quality. These effects may vary regionally depending on the biomass feedstocks and changes in land management. Here, we focused on the Tennessee River Basin (TRB), USA. According to the recent 2016 Billion‐Ton Report (BT16) by the US Department of Energy, under two future scenarios (base‐case and high‐yield), three perennial feedstocks show high potential for growing profitably in the TRB: switchgrass (Panicum virgatum), miscanthus (Miscanthus × giganteus), and willow (Salix spp.). We used the Soil & Water Assessment Tool (SWAT) to compare hydrology and water quality for a current landscape with those simulated for two future BT16 landscapes. We combined publicly available temporal and geospatial datasets with local land and water management information to realistically represent physical characteristics of the watershed. We developed a new autocalibration tool (SWATopt) to calibrate and evaluate SWAT in the TRB with reservoir operations, including comparison against synthetic and intermediate response variables derived from gage measurements. Our spatiotemporal evaluation enables to more realistically simulate the current situation, which gives us more confidence to project the effects of land‐use changes on water quality. Under both future BT16 scenarios, simulated nitrate and total nitrogen loadings and concentrations were greatly reduced relative to the current landscape, whereas runoff, sediment, and phosphorus showed only small changes. Difference between simulated water results for the two future scenarios was small. The influence of biomass production on water quantity and quality depended on the crop, area planted, and management practices, as well as on site‐specific characteristics. These results offer hope that bioenergy production in the TRB could help to protect the region's rivers from nitrogen pollution by providing a market for perennial crops with low nutrient input requirements.
Social and economic indicators can be used to support design of sustainable energy systems. Indicators representing categories of social well-being, energy security, external trade, profitability, resource conservation, and social acceptability have not yet been measured in published sustainability assessments for commercial algal biofuel facilities. We review socioeconomic indicators that have been modeled at the commercial scale or mea-sured at the pilot or laboratory scale, as well as factors that affect them, and discuss additional indicators that should be measured during commercialization to form a more complete picture of socioeconomic sustainability of algal biofuels. Indicators estimated in the scientific literature include the profitability indicators, return on investment (ROI) and net present value (NPV), and the resource conservation indicator, fossil energy return on investment (EROI). These modeled indicators have clear sustainability targets and have been used to design sustainable algal biofuel systems. Factors affecting ROI, NPV, and EROI include infrastructure, process choices, and financial assumptions. The food security indicator, percent change in food price volatility, is probably zero where agricultural lands are not used for production of algae-based biofuels; however, food-related coproducts from algae could enhance food security. The energy security indicators energy security premium and fuel price volatility and external trade indicators terms of trade and trade volume cannot be projected into the future with accuracy prior to commercialization. Together with environmental sustainability indicators, the use of a suite of socioeconomic sustainability indicators should contribute to progress toward sustainability of algal biofuels
The biobased economy is playing an increasingly important role in the American economy.
Through innovations in renewable energies and the emergence of a new generation of biobased products, the sectors that drive the biobased economy are providing job creation and economic growth. To further understand and analyze trends in the biobased economy, this report compares 2011 and 2016 report data.