The GLOBIOM-Brazil model (10) is based on the GLOBIOM model (11, 12) and has been adapted to incorporate Brazil’s specificities and local policies (22). As with GLOBIOM, GLOBIOM-Brazil is a global partial equilibrium model that simulates the competition for land among the main sectors of the land use economy (agriculture, forestry, and bioenergy) subjected to resource, technology, and policy restrictions. GLOBIOM is recursively run for 10-year time steps, starting at the baseline year of 2000. For this study, the GLOBIOM-Brazil model was adapted to run for a 5-year time step with a greater temporal resolution, which allows more flexibility in defining the starting dates of Brazil’s local policies. The competition for land was simulated at the pixel level by maximizing the sum of consumer and producer surpluses. Exogenous drivers, such as gross domestic product (GDP), population growth, and dietary trends, were derived from the SSPs (23) developed for the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. In this study, we used the “middle of the road” SSP2, which projects 71% of exogenous soybeans yield increase due to technological change, a 36% growth in Brazil’s population, and a 266% increase in Brazil’s GDP per capita by 2050 compared to 2000. According to the Brazilian Institute of Geography and Statistics (IBGE), the population in Brazil will increase from 173 million in 2000 to 223 million in 2030, a growth of 29%, which is close to the SSP2 projections for the same year (29.7%). We also used two contrasting pathways, SSP1 sustainability and SSP3 fragmentation, to analyze the uncertainties regarding the future demand for soy and soybean expansion related to changes in the socioeconomic drivers of land use change in Brazil. The SSP1 and SSP3 project, respectively, 83 and 43% of soybeans yield increase, 25 and 54% growth in Brazil’s population, and 355 and 120% increase in Brazil’s GDP per capita by 2050 compared to 2000. The bioenergy demand (biodiesel, bioethanol, charcoal, and heat and electricity) was also established exogenously per region using the World Energy Outlook 2010 projections. Brazil’s future bioethanol demand was taken from the Ministry of Mines and Energy and the Energy Research Enterprise projections (24). On the supply side, endogenous production adjustments were made by the model to meet the demand for all 30 economic regions. GLOBIOM optimizes land use over six land use classes, including croplands, pasture, unmanaged forests or native vegetation, and nonproductive land. As a result of the optimization procedure, the final demand, processing quantities, prices, and trade at the equilibrium state were obtained for each region and product. The geographically explicit representation of the model was a uniform grid with a spatial resolution of approximately 50 km by 50 km at the equator for Brazil and 250 km by 250 km for the other 29 regions of the world. The 18 crops modeled by GLOBIOM-Brazil included soybeans, maize, and sugarcane and made up 86% of the total cultivated area in Brazil in the baseline year of 2000. The potential yields for the different crops and management systems were defined at the pixel level and relied on the biophysical model EPIC (Environmental Policy Integrated Climate) (25). Productivity can increase endogenously in the model through shifts between management systems (from low to high input) or from the reallocation of production to more suitable areas, which allows endogenous changes of yields in response to market signals. A remote sensing data study (26) concluded that all maize harvested in Mato Grosso state in 2001 and 2010 was produced using a double cropping system with soybeans. In addition, according to official statistics, between 2003 and 2015, the area of single crop maize decreased approximately by 4 Mha, whereas the area of double crop maize (or safrinha maize) jumped by more than 6 Mha. Most safrinha was cultivated with soybeans. Thus, for this study, a double cropping system for soybeans and maize was included in GLOBIOM-Brazil on the basis of standard runs of the EPIC model for high-input systems. An exogenous yield increase due to future technological changes was also allowed for all crops and management systems on the basis of the economic growth projections given by the SSPs (27). Eight livestock production systems were specified for ruminants, from grazing humid to mixed arid systems. Bovine and small ruminant productivity and feed requirements were estimated by the RUMINANT model (28, 29). Switches among production systems allow for feed substitution and for the intensification or extensification of livestock production. In addition, a semi-intensive cattle ranching production system was also implemented for Brazil (30). Wood products come from forestry land use classes such as short rotation plantations. Harvesting costs and annual mean increments were computed by the forestry model G4M (31). GLOBIOM-Brazil uses a consistent 2000 land cover and land use map for Brazil. This map combines information from official statistics on livestock and crop production from different satellite images and from maps of the protected areas (10). Internal transportation costs per pixel and product to different destinations (nearest state capital and nearest seaport) were estimated on the basis of the national transport infrastructure (10).

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