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.