310 | IAASTD Global Report

made visible. Not all future developments, however, can be assessed with the various tools that are used in this subchap-ter. Subchapter 5.5 therefore describes a series of important, emerging issues related to AKST that can affect the reference world and alternative policy pathways. Subchapters 5.6 and 5.7, finally, examine synergies and tradeoffs and implica­tions for AKST in the future, respectively.

5.2 Rationale and Description of Selected Tools

The inclusion of various tools in the assessment process enables the examination of the various relationships that transpire determined by major drivers. Also synergies and tradeoffs between specific policy interventions can be made visible through the use of modeling tools. Modeling results can be used to support policy analysis in this assessment. Clearly, models cannot provide answers for all issues. In that case, qualitative translations of the modeling results are used in this Chapter to assess the most crucial policy options that have been identified in Chapter 4.

5.2.1 Rationale for model selection

In this assessment, with its focus on agriculture and the role of AKST, the partial equilibrium agricultural sector Interna­tional Model for Policy Analysis of Agricultural Commodi­ties and Trade, or IMPACT (Rosegrant et al., 2002), plays a pivotal role. Partial equilibrium agricultural sector models are capable of providing insights into long-term changes in food demand and supply at a regional level, taking into ac­count changes in trade patterns using macro-economic as­sumptions as an exogenous input. To be able to assess the environmental consequences of changes in the agricultural sector, a range of environmental models is used as well. The integrated assessment model IMAGE 2.4 (Eickhout et al., 2006) is central in this environmental assessment, while specific models like EcoOcean and GLOBIO3 (Alkemade et al., 2006) are used to provide consequences for specific issues, marine and terrestrial biodiversity, respectively. The livestock spatial location-allocation model, SLAM, (Thorn­ton et al., 2002, 2006) and the water model WATERSIM (de Fraiture 2007) are used to give specific insights in crucial sectors for agriculture and AKST. The computable general equilibrium (CGE) model GTEM (Ahammad and Mi, 2005) is used to validate the GDP and population input data to achieve cross-sectoral consistency for the reference run. The regional models, GEN-CGE for India (Sinha and Sangeetz, 2003; Sinha et al., 2003) and the Chinese Agricultural Pol­icy Simulation Model (CAPSiM) (Huang and Li, 2003), are used to add local flavors to the global analyses that have been performed by the other tools. India and China were chosen since future policy change in these two countries will affect global food supply, demand, prices, and food secu­rity. Moreover, China- and India-specific modeling tools are used to provide deeper insights about specific development goals such as the distributional aspects of equity and pov­erty which cannot be addressed by global models.

     The tools used in this assessment for the reference run out to 2050 (Table 5-1). A selection of the models is also used for the policy experiments in subchapter 5.4 (Table 5-1). Short descriptions of model types are provided below


and longer descriptions, including an assessment of major uncertainties are introduced in the appendix to this chapter. Linkages among models are presented in subchapter 5.2.2. Partial equilibrium agricultural sector models

Partial equilibrium models (PE) treat international markets for a selected set of traded goods, e.g., agricultural goods in the case of partial equilibrium agricultural sector models. These models consider the agricultural system as a closed system without linkages with the rest of the economy, apart from exogenous assumptions on the rest of the domestic and world economy. The strength of these partial equilib­rium models is their great detail of the agricultural sector. The "food" side of these models generally uses a system of supply and demand elasticities incorporated into a series of linear and nonlinear equations, to approximate the underly­ing production and demand functions. World agricultural commodity prices are determined annually at levels that clear international markets. Demand is a function of prices, income and population growth. Biophysical information on a regional level (e.g., on land or water availability), is con­straining the supply side of the model.

          Food projections' models that simulate aggregations of components—regions, commodities and larger countries— tend to be more reliable (McCalla and Revoredo, 2001). PE modeling approaches require (1) consistent and clearly defined relations among all variables, (2) a transfer of the structure of interrelationships among variables, which was consistent in the past, to the future, (3) changes in complex cross-relationships among variables over time, (4) the simul­taneous and managed interaction of many variables and the maintenance of consistent weights and (5) an organized and consistent treatment of massive numbers of variables and large amounts of data (McCalla and Revoredo, 2001).

          Food projection models make major contributions in exploring future food outcomes based on alternative as­sumptions about crucial exogenous and endogenous vari­ables. Results from alternative policy variants can be used to alert policy makers and citizens to major issues that need attention to avoid adverse food security outcomes. A test for the usefulness of these models may therefore be whether or not the analysis enriched the policy debate (McCalla and Revoredo, 2001).

          While models can make important contributions at the global and regional levels, increasingly food insecurity will be concentrated in individual countries with high popula­tion growth, high economic dependence on agriculture, poor agricultural resources and few alternative development opportunities. These countries continue to be overlooked in regional and global studies, since, on aggregate, resources are sufficient to meet future food demands.

          Whereas the methodology and underlying supply and demand functional forms are well established in the litera­ture and have been validated through projections of histori­cal trends, the driving forces and elasticities underlying the commodity and country and regional-level supply and de­mand functions towards the future continue to be debated in the literature. Moreover, income and population growth projections, as well as lasting external shocks contribute to the uncertainty of projection outcomes.