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There is an inextricable link between energy production and food/feed/fiber cultivation with available water resources. Currently in the United States, agriculture represents the largest sector of consumptivewater usemaking up 80.7%of the total. Electricity generation in the U.S. is projected to increase by 24 % in the next two decades and globally, the production of liquid transportation fuels are forecasted to triple over the next 25-years, having significant impacts on the import/export market and global economies.

Author(s):
Brandon C. Moore
Funded from the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Bioenergy Technologies Office.

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.

Author(s):
Hoskinson, R.L.

Increasing demand for crop-based biofuels, in addition to other human drivers of land use, induces direct and indirect land use changes (LUC). Our system dynamics tool is intended to complement existing LUC modeling approaches and to improve the understanding of global LUC drivers and dynamics by allowing examination of global LUC under diverse scenarios and varying model assumptions. We report on a small subset of such analyses.

Crop intensification is often thought to increase greenhouse gas (GHG) emissions, but studies in which crop management is optimized to exploit crop yield potential are rare. We conducted a field study in eastern Nebraska, USA to quantify GHG emissions, changes in soil organic carbon (SOC) and the net global warming potential (GWP) in four irrigated systems: continuous maize with recommended best management practices (CC-rec) or intensive management (CC-int) and maize–soybean rotation with recommended (CS-rec) or intensive management (CS-int).

USDA Agricultural Projections for 2011-20, released in February 2011, provide longrun projections for the farm sector for the next 10 years. These annual projections cover agricultural commodities, agricultural trade, and aggregate indicators of the sector, such as farm income and food prices.

Important assumptions for the projections include:

Author(s):
USDA Economic Research Service

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

Author(s):
USDA Economic Research Service

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.

Author(s):
Graubner, Marten

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.

Author(s):
USDA Foreign Agriculture Service

In the corn ethanol industry, the ability of plants to obtain favorable prices through marketing decisions is considered important for their overall economic performance. Based on a panel of surveyed of ethanol plants we extend data envelopment analysis (DEA) to decompose the economic efficiency of plants into conventional sources (technical and allocative efficiency) and a new component we call marketing efficiency.

Author(s):
Sesmero, Juan S.

The rapidly expanding biofuel industry has changed the fundamentals of U.S. agricultural commodity markets. Increasing ethanol and biodiesel production has generated a fast-growing demand for corn and soybean products, which competes with the well-established domestic livestock industry and foreign buyers. Meanwhile, the co-products of biofuel production are replacing or displacing coarse grains and oilseed meal in feed rations for livestock.

Author(s):
Tun-Hsiang (Edward) Yu

The location of ethanol plants is determined by infrastructure, product and input markets, fiscal attributes of local communities, and state and federal incentives. This empirical
analysis uses probit regression along with spatial clustering methods to analyze investment activity of ethanol plants at the county level for the lower U.S. 48 states from 2000 to 2007.
The availability of feedstock dominates the site selection decision. Other factors, such as access to navigable rivers or railroads, product markets, producer credit and excise tax

Author(s):
D.M. Lambert

Supply chain management involves all of the activities in industrial organizations from raw material procurement to final product delivery to customers. The main aim in supply chain management is to satisfy production requirements, while optimizing the economic objectives. In traditional fossil fuel supply chains, huge amounts of fossil fuels are transported via pipelines or tankers with very small costs. These fuels can be transformed into other sources of energy or transportation fuels at their destination points.

Author(s):
Ahu Soylu

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

Author(s):
D. Voivontas