The land cover of the Earth has a central role in many important biophysical and socioeconomic processes of global environmental change. Contemporary land cover is changed mostly by human use; therefore, understanding of land-use change is essential in understanding land-cover change (Turner et al., 1995). Land use is defined through its purpose and is characterized by management practices such as logging, ranching, and cropping. Land cover is the actual manifestation of land use (i.e., forest, grassland, cropland) (IPCC, 2000). Land-use change and land-cover change (LUC-LCC) involve several processes that are central to the estimation of climate change and its impacts (Turner et al., 1995). First, LUC-LCC influences carbon fluxes and GHG emissions (Houghton, 1995; Braswell et al., 1997). This directly alters atmospheric composition and radiative forcing properties. Second, LUC-LCC changes land-surface characteristics and, indirectly, climatic processes (Bonan, 1997; Claussen, 1997). Third, LUC-LCC is an important factor in determining the vulnerability of ecosystems and landscapes to environmental change (Peters and Lovejoy, 1992). LCC, for example—through nitrogen addition, drainage and irrigation, and deforestation (Skole and Tucker, 1993; Vitousek et al., 1997)—may alter the properties and possible responses of ecosystems. Finally, several options and strategies for mitigating GHG emissions involve land cover and changed land-use practices (IPCC, 1996b).

The central role of LUC-LCC highlights the importance of its inclusion in scenario development for assessing global change impacts. To date this has not been done satisfactorily in most assessments (Leemans et al., 1996a). For instance, in earlier emission scenarios (e.g., Leggett et al., 1992), constant emission factors were applied to define land use-related methane (CH₄) and nitrous oxide (N₂O) emissions. Furthermore, linear extrapolations of observed deforestation rates were assumed, along with an averaged carbon content in deforested areas. The SRES scenarios (Nakicenovic et al., 2000) have improved on the underlying LUC-LCC assumptions, considerably enhancing scenario consistency. Unfortunately, these SRES scenarios provide highly aggregate regional LUC-LCC information, which is difficult to use in impact assessments. A comprehensive treatment of the other roles of LUC-LCC in the climate system is still deficient. To highlight these shortcomings, this section reviews studies and approaches in which LUC-LCC information is applied to develop scenarios for both impact and mitigation assessment.

3.3.2.1. Baseline Data

The SAR evaluated land-use and land-cover data sets and concluded that they often were of dubious quality (Leemans et al., 1996a). Since the SAR, many statistical data sources have been upgraded and their internal consistency improved (e.g., FAO, 1999), although large regional differences in quality and coverage remain. In addition, the high-resolution global database, DISCover, has become available (Loveland and Belward, 1997). This database is derived from satellite data and consists of useful land-cover classes. Furthermore, attempts also have been made to develop historical land-use and land-cover databases (Ramankutty and Foley, 1999; Klein Goldewijk, 2001). These databases use proxy sources—such as historic maps, population-density estimates, and infrastructure—to approximate land-cover patterns. All of these improvements to the information base are important for initializing and validating the models used in scenario development for global change assessments.

3.3.2.2. Regional and Sector-Specific Approaches

A large variety of LUC-LCC scenarios have been constructed. Many of them focus on local and regional issues; only a few are global in scope. Most LUC-LCC scenarios, however, are developed not to assess GHG emissions, carbon fluxes, and climate change and impacts but to evaluate the environmental consequences of different agrosystems (e.g., Koruba et al., 1996), agricultural policies (e.g., Moxey et al., 1995), and food security (e.g., Penning de Vries et al., 1997) or to project future agricultural production, trade, and food availability (e.g., Alexandratos, 1995; Rosegrant et al., 1995). Moreover, changes in land-cover patterns are poorly defined in these studies. At best they specify aggregated amounts of arable land and pastures.

One of the more comprehensive attempts to define the consequences of agricultural policies on landscapes was the "Ground for Choices" study (Van Latesteijn, 1995). This study aimed to evaluate the consequences of increasing agricultural productivity and the Common Agricultural Policy in Europe and analyzed the possibilities for sustainable management of resources. It concluded that the total amount of agricultural land and employment would continue to decline—the direction of this trend apparently little influenced by agricultural policy. Many different possibilities for improving agricultural production were identified, leaving room for development of effective measures to preserve biodiversity, for example. This study included many of the desired physical, ecological, socioeconomic, and regional characteristics required for comprehensive LUC-LCC scenario development but did not consider environmental change.

Different LUC-LCC scenario studies apply very different methods. Most of them are based on scenarios from regression or process-based models. In the global agricultural land-use study of Alexandratos (1995), such models are combined with expert judgment, whereby regional and disciplinary experts reviewed all model-based scenarios. If these scenarios were deemed inconsistent with known trends or likely developments, they were modified until a satisfactory solution emerged for all regions. This approach led to a single consensus scenario of likely agricultural trends to 2010. Such a short time horizon is appropriate for expert panels; available evidence suggests that expert reviews of longer term scenarios tend to be conservative, underestimating emerging developments (Rabbinge and van Oijen, 1997).

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