Aggregation and Scaling
AgMIP research initiatives must overcome significant obstacles in scale dependence to link field-level crop models to regional and global economic models. AgMIP is developing and evaluating procedures to scale field-level outputs up to regional and country scales. Aggregation is facilitated by the availability of quality geographic data regarding the spatial distribution of climate (daily weather), topography, soils, land-use, farm-level management, socioeconomic conditions, and reported yields. While excellent data exist in some regions, data-sparse regions are often those with large spatial heterogeneity in farming conditions and practices. For these regions, AgMIP will investigate the potential of satellite, remote sensing, and other observational products to fill gaps in data. Techniques used in agricultural models that operate on scales closer to global climate model resolutions and have regional and global foci (such as GLAM, Challinor et al., 2004; LPJmL, Bondeau et al., 2007; PEGASUS, Deryng et al., 2011; IMPACT, Nelson et al., 2009; GLOBIOM, Havlík et al., 2011) will also be compared.
Aggregation of field-scale crop model outputs to a regional or larger-scale economic model generally follows one of several approaches (e.g., Hansen and Jones, 2000; Ewert et al., 2011). One approach involves disaggregating the region into approximately homogeneous sub-regions in a type of biophysical typology (Hazeu et al., 2010) with associated sentinel sites for calibrated crop model simulations, and then converting yields to regional production using planted areas in each sub-region (Burke et al., 1991; de Jager et al., 1998; Yu et al., 2010; Ruane et al., 2011). Another approach uses multivariate sensitivity tests to cast probabilistic distributions of farm-level conditions into an estimate of regional production (Haskett et al., 1995). In a third approach, farm behavior is explicitly taken into account, and crop models are linked to farm economic models to provide farm production estimates, which can subsequently be upscaled through response functions (Pérez Domínguez et al., 2009; Ewert et al., 2009). A fourth approach is to make crop model simulations at high spatial resolution but with relatively coarse management differences, potentially utilizing reported regional yields to assist in bias-correction. Relative responses to different climate futures are then aggregated up to economic units of analysis and used to adjust exogenously-determined changes in productivity (Nelson et al., 2010).
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