KDF Search Results

Logging and mill residues are currently the largest sources of woody biomass for bioenergy in the US, but short-rotation woody crops (SRWCs) are expected to become a larger contributor to biomass production, primarily on lands marginal for food production. However, there are very few studies on the environmental effects of SRWCs, and most have been conducted at stand rather than at watershed scales.

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.

Join the U.S. Department of Energy’s Bioenergy Technologies Office on Dec. 6, 2018, at 1 p.m. CST for a webinar on “Biomass Production and Water Quality in the Mississippi River Basin.” In this webinar, Argonne National Laboratory and Oak Ridge National Laboratory will jointly present modeling and analyses of potential implications of biomass production on nutrients and sediments in each of the six tributaries of the Mississippi River Basin.

Model-data comparisons are always challenging, especially when working at a large spatial scale and evaluating multiple response variables. We implemented the Soil and Water Assessment Tool (SWAT) to simulate water quantity and quality for the Tennessee River Basin.

This dataset reports the pre-treatment hydrology and pre- and post-treatment water quality data from a watershed-scale experiment that is evaluating the effects of growing short-rotation loblolly pine for bioenergy on water quality and quantity in the southeastern U.S. The experiment is taking place on the Savannah River Site, near New Ellenton, South Carolina, USA.  Beginning in 2010, water quality and hydrology were measured for two years in 3 watersheds (R, B, C).

Global development of the biofuel sector is proceeding rapidly. Biofuel feedstock continues to be produced from a variety of agricultural and forestry resources. Large-scale feedstock production for biofuels could change the landscape structure and affect water quantity, water quality, and ecosystem services in positive or negative ways. With rapid advancements in computation technologies and science, field- and watershed-scale models have become a vital tool for quantifying water quality and ecosystem responses to bioenergy landscape and management practices.

Using the Soil and Water Assessment Tool (SWAT) for large-scale watershed modeling could be useful for evaluating the quality of the water in regions that are dominated by nonpoint sources in order to identify potential “hot spots” for which mitigating strategies could be further developed. An analysis of water quality under future scenarios in which changes in land use would be made to accommodate increased biofuel production was developed for the Missouri River Basin (MoRB) based on a SWAT model application.

Water consumption and water quality continue to be key factors affecting environmental sustainability in biofuel production. This review covers the findings from biofuel water analyses published over the past 2 years to underscore the progress made, and to highlight advancements in understanding the interactions among increased production and water demand, water resource availability, and potential changes in water quality. We focus on two key areas: water footprint assessment and watershed modeling.

The increasing demand for bioenergy crops presents our society with the opportunity to design more sustainable landscapes. We have created a Biomass Location for Optimal Sustainability Model (BLOSM) to test the hypothesis that landscape design of cellulosic bioenergy crop plantings may simultaneously improve water quality (i.e. decrease concentrations of sediment, total phosphorus, and total nitrogen) and increase profits for farmer-producers while achieving a feedstock-production goal.

United States is experiencing increasing interests in fermentation and anaerobic digestion processes for the production of biofuels. A simple methodology of spatial biomass assessment is presented in this paper to evaluate biofuel production and support the first decisions about the conversion technology applications. The methodology was applied to evaluate the potential biogas and ethanol production from biomass in California and Washington states. Solid waste databases were filtered to a short list of digestible and fermentable wastes in both states.

This document contains all publication outcomes of the Sun Grant Regional Feedstock Partnership. For more information, visit www.sungrant.org

Abstract: To ensure effective biomass feedstock provision for large-scale ethanol production, a three-stage supply chain was proposed to include biomass supply sites, centralized storage and preprocessing (CSP) sites, and biorefi nery sites. A GIS-enabled biomass supply chain optimization model (BioScope) was developed to minimize annual biomass-ethanol production costs by selecting the optimal numbers, locations, and capacities of farms, CSPs, and biorefi neries as well as identifying the optimal biomass fl ow pattern from farms to biorefi neries.

A method is presented, which estimates the potential for power production from agriculture residues. A GIS decision support system (DSS) has been developed, which implements the method and provides the tools to identify the geographic distribution of the economically exploited biomass potential. The procedure introduces a four level analysis to determine the
theoretical, available, technological and economically exploitable potential. The DSS handles all possible restrictions and

The use of Geographic Information Systems (GIS) for understanding the geographic context of bioenergy supplies is discussed and a regional-scale, GIS-based modeling system for estimating potential biomass supplies from energy crops is described. While GIS models can capture geographic variation that may in?uence biomass costs and supplies, GIS models are not likely to handle uncertainty well and are often limited by the lack of spatially explicit data.

When fuelwood is harvested at a rate exceeding natural growth and inefficient conversion technologies are used, negative environmental and socio-economic impacts, such as fuelwood shortages, natural forests degradation and net GHG emissions arise. In this study, we argue that analyzing fuelwood supply/demand spatial patterns require multiscale approaches to effectively bridge the gap between national results with local situations.