modeling

The stakeholders involved in management of land and carbon (C) are diverse. Farmers and foresters are concerned with plants and management practices that are most likely to sustain profits. The opportunity to sell C sequestration credits adds a new
dimension to production strategies. Land managers may be asking questions, such as how tillage and fertilizer practices in a specific location affect C storage and crop yields. Regional planners and governing bodies may have the opportunity to influence where and how cultivation occurs and interacts with other land uses and industries. They may ask questions related to how crops can be distributed across a landscape to achieve multiple goals that reflect local priorities (water quality, scenic views, traditional lifestyles, tax revenues, etc.). At state and national levels, there are requirements to manage human activities to comply with land, water, and airemission regulations as well as policy objectives such as job creation and energy security. Decision makers at these levels may desire guidance on how the interactions of policy options provide incentives or disincentives for certain land-use practices and resulting environmental and socioeconomic impacts. Many decision makers are most interested in how scientific information can be used to guide land-use practices in the near term, typically one to five years. However, the scientific information may derive from data measured at entirely different scales or locations and in time spans that range from decades to centuries. With rising attention to global markets and climate change, managers are concerned about how changes in their region are affected by global processes. National and regional decision makers want to know how their choices affect productivity, incomes, C and nutrient cycles, and other development goals. There needs to be a better match between the diverse needs of managers and the information provided by scientific analysis and models. Models are an important tool in scientific investigations. Britain’s Science Council defines science to be “the pursuit of knowledge and understanding of the natural and social world following a systematic methodology based on evidence.”1 Systems for observing, documenting, and analyzing results are organized under many different disciplines, which share the common thread of being built around observation and measurement. Careful monitoring and measurement leads to newdiscoveries, new and revised hypotheses, tests of those hypotheses, and, hence, better science. Disciplined measurements that use accepted protocols have much more than a supporting role for science – they form its very foundation. However, for many practical, financial,
logistic, and physical reasons, not everything can be observed and measured. For example, some changes occur over decades, centuries, or millennia, and others occur on very large areas, but most measurements record short-term changes in a relatively small area. Support for long-term or large-scale monitoring is scanty and difficult to obtain. Furthermore, the causes and effects of complex relationships are often difficult to discern and change over time, making research results dependent on the temporal and spatial scales of analysis. Therefore, models that are properly designed and used can play a valuable role in elucidating long-term, large-scale, or complex processes.  Models are a tool that can be used to explore scientific hypotheses. RayOrbach likened science to a three-legged stool, the legs of which are theory, experiment, andmodeling and simulation (personal communication). All three legs depend on foundations of data.

This chapter describes ways to use models as a bridge between scientific understanding of land-use practices and C flux and the needs of decision makers regarding management of land and C. To do so, we explore the modeling process and types of models that are used for land and C. That topic sets the context for a discussion of the advantages of using models to increase understanding of decision makers about land and C processes as well as cautionary principles. The next section reveals how scientists can best communicate modeling results to decision makers and what decision makers should ask of models. This analysis leads to some recommended practices and a conclusion about the next steps that should be taken to foster improved integration between science and management via models. Because of the diversity of stakeholders involved in these issues, the audience for this chapter is quite broad.  Chapter 7 discusses how C is a part of land-use models, and several chapters review  and analyze how information related to land use and the C cycle are monitored and measured.

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. A number of modeling efforts based on these assumptions have projected significant iLUC from the increases in the use of corn for ethanol production. The current study tests the veracity of these assumptions through a systematic decomposition analysis of the empirical data from 2001 to 2009. The logarithmic mean divisia index decomposition method (Type I) was used to estimate contributions of different factors to meeting the corn demand for ethanol production. Results show that about 79% of the change in corn used for ethanol production can be attributed to changes in the distribution of domestic corn consumption among different uses. Increases in the domestic consumption share of corn supply contributed only about 5%. The remaining contributions were 19% from added corn production, and -2% from stock changes. Yield change accounted for about two thirds of the contributions from production changes. Thus, the results of this study provide little support for large land-use changes or diversion of corn exports because of ethanol production in the United States furing the past decade.

Biofuels are promoted in the United States through aggressive legislation, as one part of an overall strategy to lessen dependence on imported energy as well as to reduce the emissions of greenhouse gases (Office of the Biomass Program and Energy Efficiency and Renewable Energy, 2008). For example, the Energy Independence and Security Act of 2007 (EISA) mandates 36 billion gallons of renewable liquid transportation fuel in the U.S. marketplace by the year 2022 (U.S. Government, 2007). Meeting such large volumetric targets has prompted an unprecedented increase in funding for biofuels research.

This paper describes the current Biomass Scenario Model (BSM) as of August 2013, a system dynamics model developed under the support of the U.S. Department of Energy (DOE). The model is the result of a multi-year project at the National Renewable Energy Laboratory (NREL). It is a tool designed to better understand biofuels policy as it impacts the development of the supply chain for biofuels in the United States. In its current form, the model represents multiple pathways leading to the production of fuel ethanol as well as advanced biofuels such as biomass-based gasoline, diesel, jet fuel, and butanol).

Switchgrass (Panicum virgatum L.) is a perennial grass native to the United States that has been studied as a sustainable source of biomass fuel. Although many field-scale studies have examined the potential of this grass as a bioenergy crop, these studies have not been integrated. In this study, we present an empirical model for switchgrass yield and use this model to predict yield for the conterminous United States. We added environmental covariates to assembled yield data from field trials based on geographic location. We developed empirical models based on these data. The resulting empirical models, which account for spatial autocorrelation in the field data, provide the ability to estimate yield from factors associated with climate, soils, and management for both lowland and upland varieties of switchgrass. Yields of both ecotypes showed quadratic responses to temperature, increased with precipitation and minimum winter temperature, and decreased with stand age. Only the upland ecotype showed a positive response to our index of soil wetness and only the lowland ecotype showed a positive response to fertilizer. We view this empirical modeling effort, not as an alternative to mechanistic plant-growth modeling, but rather as a first step in the process of functional validation that will compare patterns produced by the models with those found in data. For the upland variety, the correlation between measured yields and yields predicted by empirical models was 0.62 for the training subset and 0.58 for the test subset. For the lowland variety, the correlation was 0.46 for the training subset and 0.19 for the test subset. Because considerable variation in yield remains unexplained, it will be important in the future to characterize spatial and local sources of uncertainty associated with empirical yield estimates.

A primary objective of current U.S. biofuel law – the “Energy Independence and Security Act of 2007” (EISA) – is to reduce dependence on imported oil, but the law also requires biofuels to meet carbon emission reduction thresholds relative to petroleum fuels. EISA created a renewable fuel standard with annual targets for U.S. biofuel use that climb gradually from 9 billion gallons per year in 2008 to 36 billion gallons (or about 136 billion liters) of biofuels per year by 2022. The most controversial aspects of U.S. biofuel policy have centered on the global social and environmental implications of land use. In particular, there is an ongoing debate about whether “indirect land use change” (ILUC) would cause biofuels to become a net source, rather than sink, of carbon emissions. Estimates of ILUC induced by biofuel production can only be inferred through modeling. This paper evaluates how model structure, underlying assumptions, and the representation of policy instruments influence the results of U.S. biofuel policy simulations. The analysis shows that differences in these factors can lead to divergent model estimates of land use and economic effects. Model estimates of the net conversion of forests and grasslands induced by U.S. biofuel policy range from 0.09 ha/1000 gallons described in this paper to 0.73 ha/1000 gallons from early studies in the ILUC change debate. We note that several important factors governing LUC change remain to be examined. Challenges that must be addressed to improve global land use change modeling are highlighted.