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For several years the Idaho National Laboratory (INL) has been developing a Decision Support System for Agriculture (DSS4Ag) which determines the economically optimum recipe of various fertilizers to apply at each site in a field to produce a crop, based on the existing soil fertility at each site, as well as historic production information and current prices of fertilizers and the forecast market price of the crop at harvest. In support of the growing interest in agricultural crop residues as a bioenergy feedstock, we have extended the capability of the DSS4Ag to develop a variable-rate fertilizer recipe for the simultaneous economically optimum production of both grain and straw. In this paper we report the results of 2 yr of field research testing and enhancing the DSS4Ag?s ability to economically optimize the fertilization for the simultaneous production of both grain and its straw, where the straw is an agricultural crop residue that can be used as a biofeedstock. For both years, the DSS4Ag reduced the cost and amount of fertilizers used and increased grower profit, while reducing the biomass produced. The DSS4Ag results show that when a biorefinery infrastructure is in place and growers have a strong market for their straw it is not economically advantageous to increase fertilization in order to try to produce more straw. This suggests that other solutions, such as single-pass selective harvest, must be implemented to meet national goals for the amount of biomass that will be available for collection and use for bioenergy.

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. We examine land allocation by type of crop, and pasture use for countries growing feedstock for ethanol (corn, sorghum, wheat, sugarcane, and other grains) and major crops competing with feedstock for land resources such as oilseeds. We shock the model with exogenous changes in ethanol demand, first in the United States, then in Brazil, China, the European Union-25, and India, and compute shock multipliers for land allocation decisions for crops and countries of interest. The multipliers show at the margin how sensitive land allocation is to the growing demand for ethanol. Land moves away from major crops and pasture competing for resources with feedstock crops. Because of the high U.S. tariff on ethanol, higher U.S. demand for ethanol translates into a U.S. ethanol production expansion. The latter has global effects on land allocation as higher coarse grain prices transmit worldwide. Changes in U.S. coarse grain prices also affect U.S. wheat and oilseed prices, which are all transmitted to world markets. In contrast, expansion in Brazil ethanol use and production chiefly affects land used for sugarcane production in Brazil and to a lesser extent in other sugarproducing countries, but with small impacts on other land uses in most countries.

IMPACT – the International Model for Policy Analysis of Agricultural Commodities and Trade – was developed at IFPRI at the beginning of the 1990s, upon the realization that there was a lack of long-term vision and consensus among policy makers and researchers about the actions that are necessary to feed the world in the future, reduce poverty, and protect the natural resource base. In 1993, these same long-term global concerns launched the 2020 Vision for Food, Agriculture and the Environment Initiative. This initiative created the opportunity for further development of the IMPACT model, and in 1995 the first results using IMPACT were published as a 2020 Vision discussion paper: Global Food Projections to 2020: Implications for Investment (Rosegrant, Agcaoili-Sombilla and Perez, 1995), in which the effects of population, investment, and trade scenarios on food security and nutrition status, especially in developing countries, were analyzed.

Land use change models are tools to support the analysis of the causes and consequences of land use dynamics. Scenario analysis with land use models can support land use planning and policy. Numerous land use models are available, developed from different disciplinary backgrounds. This paper reviews current models to identify priority issues for future land use change modelling research. This discussion is based on six concepts important to land use modelling: (1) Level of analysis; (2) Cross-scale dynamics; (3) Driving forces; (4) Spatial interaction and neighbourhood effects; (5) Temporal dynamics; and (6) Level of integration. For each of these concepts an overview is given of the variety of methods used to implement these concepts in operational models. It is concluded that a lot of progress has been made in building land use change models. However, in order to incorporate more aspects important to land use modelling it is needed to develop a new generation of land use models that better address the multi-scale characteristics of the land use system, implement new techniques to quantify neighbourhood effects, explicitly deal with temporal dynamics and achieve a higher level of integration between disciplinary approaches and between models studying urban and rural land use changes. If these requirements are fulfilled models will better support the analysis of land use dynamics and land use policy formulation.

The Emissions Prediction and Policy Analysis (EPPA) model is the part of the MIT Integrated Global Systems Model (IGSM) that represents the human systems. EPPA is a recursive-dynamic multi-regional general equilibrium model of the world economy, which is built on the GTAP dataset and additional data for the greenhouse gas and urban gas emissions. It is designed to develop projections of economic growth and anthropogenic emissions of greenhouse related gases and aerosols. The main purpose of this report is to provide documentation of a new version of EPPA, EPPA version 4. In comparison with EPPA3, it includes greater regional and sectoral detail, a wider range of advanced energy supply technologies, improved capability to represent a variety of different and more realistic climate policies, and enhanced treatment of physical stocks and flows of energy, emissions, and land use to facilitate linkage with the earth system components of the IGSM. Reconsideration of important parameters and assumptions led to some revisions in reference projections of GDP and greenhouse gas emissions. In EPPA4 the global economy grows by 12.5 times from 2000 to 2100 (2.5%/yr) compared with an increase of 10.7 times (2.4%/yr) in EPPA3. This is one of the important revisions that led to an increase in CO2 emissions to 25.7 GtC in 2100, up from 23 GtC in 2100 projected by EPPA3. There is considerable uncertainty in such projections because of uncertainty in various driving forces. To illustrate this uncertainty we consider scenarios where the global GDP grows 0.5% faster (slower) than the reference rate, and these scenarios result in CO2 emissions in 2100 of 34 (17) GtC. A sample greenhouse gas policy scenario that puts the world economy on a path toward stabilization of atmospheric CO2 at 550 ppmv is also simulated to illustrate the response of EPPA4 to a policy constraint.

The Targets IMage Energy Regional simulation model, TIMER, is described in detail. This model was developed and used in close connection with the Integrated Model to Assess the Global Environment (IMAGE) 2.2. The system-dynamics TIMER model simulates the global energy system at an intermediate level of aggregation. The model can be used on a stand-alone basis or integrated within the framework of the integrated assessment model IMAGE 2.2. The model simulates the world on the basis of 17 regions. The main objectives of TIMER are to analyse the long-term dynamics of energy conservation and the transition to non-fossil fuels within an integrated modelling framework, and explore long-term trends for energy-related greenhouse gas emissions. Important components of the various submodels are: price-driven fuel and technology substitution processes, cost decrease as a consequence of accumulated production (?learning-by-doing?), resource depletion as a function of cumulated use (long-term supply cost curves) and price-driven fuel trade. The first chapter gives a brief overview of the model objective, set-up and calibration method. In subsequent chapters, the various submodels are discussed, with the introduction of introduciconcepts, equations, input assumptions and calibration results. Chapter 3 deals with the Energy Demand submodel, Chapter 4 with the Electric Power Generation submodel, and Chapters 5 and 6 with the Fuel Supply submodels. Chapter 7 describes fuel trade and technology transfer modelling; Chapter 8, the Emissions submodel. In the last chapter, a few generic concepts are discussed in some detail to improve the user?s understanding of the model. The TIMER-model has played a role in the following: the Special Report on Emission Scenarios (SRES) for the Intergovernmental Panel on Climate Change (IPCC), the European AirClim-project, the construction of global mitigation scenarios, and the Policy Options for CO2 Emission Mitigation in China project.