In this section we describe how land use it modelled in WITCH. Given the importance of land use emissions, of the link between agriculture, biomass energy and forest management, modelling land-use is of key importance in integrated assessment models. Rather than being modelled in its full detail, land-use in WITCH is represented by the mean response functions produced by the Global Biosphere Management Model (GLOBIOM) land-use model (Havlik et al. 2014). GLOBIOM is a partial equilibrium model that covers agriculture and forestry, including bioenergy. It is used for analysing land-use scenarios over many years. In GLOBIOM, the world is divided into 30 economic regions, in which consumer behaviour is modelled through isoelastic demand functions. Commodity uses “Simulation Units”, which are aggregates of 5 to 30 arcmin pixels belonging to the same altitude, slope, and soil class in the same country. For crops, grass, and forest products, Leontief production functions covering alternative production systems are calibrated from biophysical models including EPIC (Izaurralde et al. 2006). Economic optimization is based on a spatial equilibrium approach and regional price-quantity equilibria are computed. The model is calibrated to year 2000 activity levels and then recursively solved in 10-year time steps from 2000 to 2050 (Herrero et al. 2014).
The following categories are included in GLOBIOM and its sectoral representation:
GLOBIOM incorporates a detailed representation of the global livestock sector (Havlik et al. 2014). Distinctions are made among dairy and other bovines, dairy and other sheep and goats, laying hens and broilers, and pigs. Livestock production activities are defined by production systems: for ruminants, grass-based (arid, humid, and temperate/highlands), mixed crop-livestock (arid, humid, and temperate/highlands), and other; for monogastrics, smallholders and industrial. For each species, production system and region, a set of input-output parameters is calculated. Feeds consist of grass, crop residues, grain concentrates, and other feedstuffs. Outputs include four meat types (beef, sheep and goat meat, poultry and pork), milk, and eggs, and environmental factors (manure production, N excretion, and GHG emissions). Switches among production systems allow for feed substitution and for intensification or extensification of livestock production.
6.0.2 Land use
GLOBIOM defines six land types: cropland (arable and perennial), grassland, short-rotation tree plantations, managed forest, unmanaged forest and other natural vegetation. Depending on the profitability of activities by land type, land can move from one type to another subject to boundary conditions. Comprehensive greenhouse gas quantities are calculated for each land type by activity.
6.0.3 Crop yields
For the three SSPs, projected yields for 2010-2100 are implemented in the GLOBIOM model through collaboration with the ISI-MIP framework. In GLOBIOM, spatial expansion of crops goes into less productive land. Moreover, cities take away the best land and push agriculture towards more marginal land. Spatial results from the biophysical crop simulation model EPIC are modified by an exogenous technological factor, which is calibrated to GDP growth. This calibration allows yield increases as a function of the change from good cropland to marginal cropland.
6.0.4 Nitrogen fertilizers
The use of nitrogen fertilizers is derived from crop input-output tables that are quantified in the narratives. Recent studies show that no major change in mineral N use efficiency can be evidenced at the global scale and for the main annual crops (J.-F. Soussana, personal communication). For N fertilizer, SSP factors affect the ratio of N fertilizer-supply increase to crop grain DM yield increase in relative units. The following values were set by SSP: SSP1: 0.75; SSP2: 1.00; and SSP3: 1.25. These modifiers are applied initially in the same way for all regions.
6.0.5 Pasture productivity
GLOBIOM defines pasture productivity from the EPIC model and from the CENTURY model. Initial values of pasture productivity are calibrated from survey data. Feed ratios are standardized by system and by region.
6.0.6 Food wastes and agricultural losses
The three SSPs are based on FAO (2011), which includes four categories of farm product losses. Post-harvest losses are functions of GDP growth under the assumption that waste-saving technologies are cheaper and more widely available in high-income nations.
Havlik, P., H. Valin, M. Herrero, M. Obersteiner, E. Schmid, M. C. Rufino, A. Mosnier, et al. 2014. “Climate Change Mitigation Through Livestock System Transitions.” Proceedings of the National Academy of Sciences 111 (10): 3709–14.
Izaurralde, RC, Jimmy R Williams, William B McGill, Norman J Rosenberg, and MC Quiroga Jakas. 2006. “Simulating Soil c Dynamics with Epic: Model Description and Testing Against Long-Term Data.” Ecological Modelling 192 (3): 362–84.
Herrero, Mario, Petr Havlik, J McIntire, Amanda Palazzo, and Hugo Valin. 2014. “African Livestock Futures: Realizing the Potential of Livestock for Food Security, Poverty Reduction and the Environment in Sub-Saharan Africa.”