KDF Search Results

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.

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.

In recent years, considerable concern has been raised about the sustainability of the world's forested ecosystems (FAO, 2003). With deforestation rates in tropical regions estimated to be as high as 12 million hectares per year (FAO, 2003; Houghton, 2003), much of the concern has centered around tropical deforestation. In contrast to these developments in tropical areas, there is evidence that the area of forests in temperate regions is expanding.

The Center for Sustainability and the Global Environment (SAGE) at the University of Wisconsin has been developing global databases of contemporary and historical agricultural land use and land cover. SAGE has chosen to focus on agriculture because it is clearly the predominant land use activity on the planet today, and provides a vital service?i.e., food?for human societies. SAGE has developed a ?data fusion?

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).

The preceding two chapters of this volume have discussed physical and economic data bases for global agriculture and forestry, respectively. These form the foundation for the integrated, global land use data base discussed in this chapter. However, in order to utilize these data for global CGE analysis, it is first necessary to integrate them into a global, general equilibrium data base. This integration is the subject of the present chapter

This paper describes the GTAP land use data base designed to support integrated assessments of the potential for greenhouse gas mitigation. It disaggregates land use by agro-ecological zone (AEZ). To do so, it draws upon global land cover data bases, as well as state-of-the-art definition of AEZs from the FAO and IIASA. Agro-ecological zoning segments a parcel of land into smaller units according to agro-ecological characteristics, including: precipitation, temperature, soil type, terrain conditions, etc. Each zone has a similar combination of constraints and potential for land use.

The paper describes the on-going project of the GTAP land use data base. We also present the GTAPE-AEZ model, which illustrates how land use and land-based emissions can be incorporated in the CGE framework for Integrated Assessment (IA) of climate change policies. We follow the FAO fashion of agro-ecological zoning (FAO, 2000; Fischer et al, 2002) to identify lands located in six zones. Lands located in a specific AEZ have similar (or homogenous) soil, landform and climatic characteristics.

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.

FAOSTAT provides time-series and cross sectional data relating to food and agriculture for some 200 countries.

The national version of FAOSTAT, CountrySTAT, is being developed and implemented in a number of target countries, primarily in sub-saharan Africa. It will offer a two-way data exchange facility between countries and FAO as well as a facility to store data at the national and sub-national levels.

This database contains current and historical official USDA data on production, supply and distribution of agricultural commodities for the United States and key producing and consuming countries.

Converting forest lands into bioenergy agriculture could accelerate climate change by emitting carbon stored in forests, while converting food agriculture lands into bioenergy agriculture could threaten food security. Both problems are potentially avoided by using abandoned agriculture lands for bioenergy agriculture. Here we show the global potential for bioenergy on abandoned agriculture lands to be less than 8% of current primary energy demand, based on historical land use data, satellite-derived land cover data, and global ecosystem modeling.

Many investigators need and use global land cover maps for a wide variety of purposes. Ironically, after many years of very limited availability, there are now multiple global land cover maps and it is not readily apparent (1) which is most useful for particular applications or (2) how to combine the different maps to provide an improved dataset. The existing global land cover maps at 1 km spatial resolution have arisen from different initiatives and are based on different remote sensing data and employed different methodologies. Perhaps more significantly, they have different legends.

Two of the most widely used land-cover data sets for the United States are the National Land-Cover Data (NLCD) at 30-m resolution and the Global Land- Cover Characteristics (GLCC) at 1-km nominal resolution. Both data sets were produced around 1992 and expected to provide similar land-cover information. This study investigated the spatial distribution of NLCD within major GLCC classes at 1-km unit over a total of 11 agricultural-related eco-regions across the continental United States.