KDF Search Results

Quantifying lignin and carbohydrate composition of corn (Zea mays L.) is important to support the emerging cellulosic biofuels industry. Therefore, field studies with 0 or 100 % stover removal were established in Alabama and South Carolina as part of the Sun Grant Regional Partnership Corn Stover Project. In Alabama, cereal rye (Secale cereale L.) was also included as an additional experimental factor, serving as a winter cover crop.

Many questions have surfaced regarding short-and long-term impacts of corn (Zea mays L.) residue removal for use in the biofuels industry. To address these concerns, a field study was established in eastern South Dakota in 2000 using no-till soil management within a 2-yr corn/soybean [Glycine max (L.) Merr.] rotation.

The use of corn for ethanol production in the United States quintupled between 2001 and 2009, generating concerns that this could lead to the conversion of forests and grasslands around the blobe, known as indirect land-use change (iLUC). Estimates of iLUC and related "food versus fuel" concerns rest on the assumption that the corn used for ethanol production in the United States would come primarily from displacing corn exports and land previously used for other crops.

ORNL Report ORNL/TM-2010-120.

We quantify the emergence of biofuel markets and its impact on U.S. and world agriculture for the coming decade using the multi-market, multi-commodity international FAPRI (Food and Agricultural Policy Research Institute) model. The model incorporates the trade-offs between biofuel, feed, and food production and consumption and international feedback effects of the emergence through world commodity prices and trade.

Agricultural activities have dramatically altered our planet?s land surface. To understand the extent and spatial distribution of these changes, we have developed a new global data set of croplands and pastures circa 2000 by combining agricultural inventory data and satellite-derived land cover data. The agricultural inventory data, with much greater spatial detail than previously available, is used to train a land cover classification data set obtained by merging two different satellite-derived products (Boston University?s MODIS-derived land cover product and the GLC2000 data set).

This study presents the results of comparing land use estimates between three different data sets for the Upper Mississippi River Basin (UMRB). The comparisons were performed between the U.S. Department of Agriculture (USDA) Natural Resource Conservation Service (NRCS) National Resource Inventory (NRI), the U.S. Geological Survey (USGS) National Land Cover Data (NLCD) database, and a combined USDA National Agricultural Statistics Service (NASS) Agricultural Census – NLCD dataset created to support applications of the Hydrologic Unit Model for the U.S. (HUMUS).

Growing concern about climate change and energy security has led to increasing interest in developing renewable, domestic energy sources for meeting electricity, heating and fuel needs in the United States. Illinois has significant potential to produce bioenergy crops, including corn, soybeans, miscanthus (Miscanthus giganteus), and switchgrass (Panicum virgatum). However, land requirements for bioenergy crops place them in competition with more traditional agricultural uses, in particular food production.

Discussions of alternative fuel and propulsion technologies for transportation often overlook the infrastructure required to make these options practical and cost-effective. We estimate ethanol production facility locations and use a linear optimization model to consider the economic costs of distributing various ethanol fuel blends to all metropolitan areas in the United States. Fuel options include corn-based E5 (5% ethanol, 95% gasoline) to E16 from corn and switchgrass, as short-term substitutes for petroleum-based fuel.

Land-use change models are important tools for integrated environmental management. Through scenario analysis they can help to identify near-future critical locations in the face of environmental change. A dynamic, spatially explicit, land-use change model is presented for the regional scale: CLUE-S. The model is specifically developed for the analysis of land use in small regions (e.g., a watershed or province) at a fine spatial resolution.

A methodology was developed to estimate quantities of crop residues that can be removed while maintaining rain or wind erosion at less than or equal to the tolerable soil-loss level. Six corn and wheat rotations in the 10 largest corn-producing states were analyzed. Residue removal rates for each rotation were evaluated for conventional, mulch/reduced, and no-till field operations.

Land-use change models are used by researchers and professionals to explore the dynamics and drivers of land-use/land-cover change and to inform policies affecting such change. A broad array of models and modeling methods are available to researchers, and each type has certain advantages and disadvantages depending on the objective of the research. This report presents a review of different types of models as a means of exploring the functionality and ability of different approaches.

This paper presents an overview of multi-agent system models of land-use/cover change (MAS/LUCC models). This special class of LUCC models combines a cellular landscape model with agent-based representations of decisionmaking, integrating the two components through specification of interdependencies and feedbacks between agents and their environment. The authors review alternative LUCC modeling techniques and discuss the ways in which MAS/LUCC models may overcome some important limitations of existing techniques. We briefly review ongoing MAS/LUCC modeling efforts in four research areas.

Until recently, advanced very high-resolution radiometer (AVHRR) observations were the only viable source of data for global land cover mapping. While many useful insights have been gained from analyses based on AVHRR data, the availability of moderate resolution imaging spectroradiometer (MODIS) data with greatly improved spectral, spatial, geometric, and radiometric attributes provides significant new opportunities and challenges for remote sensing-based land cover mapping research.

This model was developed at Idaho National Laboratory and focuses on crop production. This model is an agricultural cultivation and production model, but can be used to estimate biomass crop yields.

Traffic flows in the U.S. have been affected by the substantial increase and, as of January 2009, decrease in biofuel production and use. This paper considers a framework to study the effect on grain transportation flows of the 2005 Energy Act and subsequent legislation, which mandated higher production levels of biofuels, e.g. ethanol and biodiesels. Future research will incorporate changes due to the recent economic slowdown.

This paper examines the impact of biofuel expansion on grain utilization and distribution at the state and cropping district level as most of grain producers and handlers are directly influenced by the local changes. We conducted a survey to understand the utilization and flows of corn, ethanol and its co-products, such as dried distillers grains (DDG) in Iowa. Results suggest that the rapidly expanding ethanol industry has a significant impact on corn utilization in Iowa.

PEATSim (Partial Equilibrium Agricultural Trade Simulation) is a dynamic, partial equilibrium, mathematical-based model that enables users to reach analytical solutions to problems, given a set of parameters, data, and initial
conditions. This theoretical tool developed by ERS incorporates a wide range of domestic and border policies that enables it to estimate the market and trade effects of policy changes on agricultural markets. PEATSim captures

Agricultural markets often feature significant transport costs and spatially distributed production and processing which causes spatial imperfect competition. Spatial economics considers the firms’ decisions regarding location and spatial price strategy separately, usually on the demand side, and under restrictive assumptions. Therefore, alternative approaches are needed to explain, e.g., the location of new ethanol plants in the U.S. at peripheral as well as at central locations and the observation of different spatial price strategies in the market.