Agricultural economy/Description

Component is implemented in:

MAGNET model (version IMAGE-MAGNET 3.0) Components: Agricultural economy

Related IMAGE components

Drivers Land-use allocation

Projects/Applications

AgMIP and ISI-MIP project FOODSECURE Global Land Outlook 1 Global Land Outlook 2 LUC4C Rethinking Biodiversity Strategies (2010) project Roads from Rio+20 (2012) project Shared Socioeconomic Pathways - SSP (2014) project Sim4Nexus The Protein Puzzle (2011) project

Key publications

Bijl et al., 2017 Stehfest et al., 2013 Von Lampe et al., 2014 Woltjer et al., 2014

References

Alexandratos and Bruinsma, 2012 Armington, 1969 Banse et al., 2008 Britz, 2003 Bruinsma, 2003 Eickhout et al., 2009 Gustavsson et al., 2011 Gustavsson et al., 2013 Helming et al., 2010 Hertel, 1997 Huang et al., 2004 Overmars et al., 2014 Van Meijl et al., 2006 Woltjer et al., 2011

Flowchart Agricultural economy. See also the Input/Output Table on the introduction page.

Model description of Agricultural economy

The MAGNET model (Woltjer et al., 2014) is based on the standard GTAP model (Hertel, 1997), which is a multi-regional, static, applied computable general equilibrium (CGE) model based on neoclassical microeconomic theory. Although the model covers the entire economy, there is a special focus on agricultural sectors. It is a further development of GTAP regarding land use, household consumption, livestock, food, feed and energy crop production, and emission reduction from deforestation or afforestation.

Demand and supply

Household demand for agricultural products is calculated based on changes in income, income elasticities, preference shift, price elasticities, cross-price elasticities, and the commodity prices arising from changes in the supply side. Demand and supply are balanced via prices to reach equilibrium. Income elasticities for agricultural commodities are consistent with FAO estimates (Britz, 2003), and dynamically depend on purchasing power parity (PPP) corrected GDP per capita. The supply of all commodities is modelled by an input–output structure that explicitly links the production of goods and services for final consumption via different processing stages back to primary products (crops and livestock products) and resources. At each production level, input of labour, capital, and intermediate input or resources (e.g., land) can be substituted for one another. For example, labour, capital and land are input factors in crop production, and substitution of these production factors is driven by changes in their relative prices. If the price of one input factor increases, it is substituted by other factors, following the price elasticity of substitution.

Regional aggregation and trade

MAGNET is flexible in its regional aggregation (140 regions). In linking with IMAGE, MAGNET distinguishes 28 individual large world regions, closely matching the regions in IMAGE (Figure IMAGE regions). Slightly more detail is provided the European regions in order to properly model the EU single market. Similar to most other CGE models, MAGNET assumes that products traded internationally are differentiated according to country of origin. Thus, domestic and foreign products are not identical, but are imperfect substitutes (Armington assumption; Armington, 1969).

Land use

In addition to the standard GTAP model, MAGNET includes a dynamic land-supply function (Van Meijl et al., 2006) that accounts for the availability and suitability of land for agricultural use, based on information from IMAGE (see below). A nested land-use structure accounts for the differences in substitutability of the various types of land use (Huang et al., 2004; Van Meijl et al., 2006). In addition, MAGNET includes international and EU agricultural policies, such as production quota and export/import tariffs (Helming et al., 2010).

Biofuel crops

MAGNET includes ethanol and biodiesel as first-generation biofuels made from wheat, sugar cane, maize, and oilseeds (Banse et al., 2008) and the use of by-products (DDGS, oilcakes) from biofuel production in the livestock sector. Second-generation biofuels are also included, with the potential amount of residues available from IMAGE/TIMER (Daioglou et al., 2016).

Livestock

MAGNET distinguishes the livestock commodities of beef cattle, dairy cattle, other cattle (sheep & goats), dairy cattle, poultry, and pig and other animal products. The first three are the ruminant sectors which are grass and crop fed, while the poultry and pigs sectors are crop fed. Modelling the livestock sector includes different feedstuffs, such as feed crops, co-products from biofuels (oil cakes from rapeseed-based biofuel, or distillers grain from wheat-based biofuels), and grass (Woltjer, 2011). Grass may be substituted by feed from crops for ruminants.

Land supply

In MAGNET, land supply is calculated using a land-supply curve that relates the area in use for agriculture to the land price. Total land supply includes all land that is potentially available for agriculture, where crop production is possible under soil and climatic conditions, and where no other restrictions apply such as urban or protected area designations (see also Component Land-use allocation). In the IMAGE model, total land supply for each region is obtained from potential crop productivity and land availability on a resolution of 5x5 arcminutes (Mandryk et al., 2015). The supply curve depends on total land supply, current agricultural area, current land price, and estimated price elasticity of land supply in the starting year. Regions differ with regard to the proportion of land in use, and with regard to change in land prices in relation to changes in agricultural land use.

Reduced land availability

By restricting land supply in IMAGE and MAGNET, the models can assess scenarios with additional protected areas, or reduced emissions from deforestation and forest degradation (REDD). These areas are excluded from the land supply curve in MAGNET, leading to lower elasticities, less land-use change and higher prices, and are also excluded from the allocation of agricultural land in IMAGE (e.g., Overmars et al., 2014, Doelman et al., 2018).

Intensification of crop and pasture production

Crop and pasture yields in MAGNET may change as a result of the following four processes:

1. autonomous technological change (external scenario assumption);
2. intensification due to the substitution of production factors (endogenous);
3. climate change (from IMAGE);
4. change in agricultural area affecting crop yields (such as, decreasing average yields due to expansion into less suitable regions; from IMAGE).

Biophysical yield effects due to climate and area changes are calculated by the IMAGE crop model and communicated to MAGNET. Likewise, also the potential yields and thus the yield gap can be assessed with the crop model in IMAGE. External assumptions on autonomous technological changes are mostly based on FAO projections (Alexandratos and Bruinsma, 2012), which describe per region and commodity, the assumed future changes in yields for a wide range of crop types. In MAGNET, the biophysical yield changes are combined with the autonomous technological change to give the total exogenous yield change. In addition, during the simulation period, MAGNET calculates an endogenous intensification as a result of price-driven substitution between labour, land and capital. Projections of crop yield increase in IMAGE-MAGNET and other global agricultural models were evaluated recently (Van Zeist et al., 2020). In IMAGE, regional yield changes due to autonomous technological change and endogenous intensification according to MAGNET are used in the spatially explicit allocation of land use (Component Land-use allocation).

Food Waste Reduction

In earlier work like for the SSP scenarios, we applied a uniform reduction of food waste (Stehfest et al. 2019, Doelman et al., 2018). From IMAGE 3.2 onwards, high- and medium-income regions can reduce food waste to achieve the lowest level among them (we typically do not adress food waste in low-income regions, where food waste tends to be low and largely occurring on the farm level). This implementation is based on Gustavsson et al. (2011; 2013), from where we derive the levels of food waste for five commodity types (cereals, other_plant_based, meat, fish_seafood, melk_eggs) and three food supply chain steps (primary, processing, consumption). Then, we find the lowest food waste level among high- and medium-income regions for each commodity type and food supply chain step and define them as food waste targets. Then, we calculate the change in the production efficiency that resembles the food waste reduction necessary for the regions to achieve their food waste targets. Finally, we calculate the MAGNET shock required to meet the targets and run MAGNET with the new production efficiencies, which resemble the reduction in food waste. The reduction in food waste reduces the pressure on the food system resulting in less agricultural land use, lower GHG emissions and reduced food prices.

Diet Changes

Another way to reduce the impact of food consumption on the environment is by adopting healthier diets, as also indicated in earlier work from the IMAGE team (e.g. Stehfest et al. 2009, 2013). These diets could be entirely plant-based or have reduced in meat and dairy consumption, or follow prescribed healthy diets addressing all food groups (Willet et al. 2019). In all cases, the changes especially in livestock-based commodities lead to reduced agricultural land use, and modifications in trade and production systems. Furthermore, in some scenarios we also explore the substitution of meat through artificial meat (Van Vuuren et al., 2018). These changes in consumption are prescribed for MAGNET, where also substitutions with other food commodities are specified.