Agriculture - IMAGE

From IAMC-Documentation
Jump to: navigation, search

Model Documentation - IMAGE

Corresponding documentation
Previous versions
Model information
Model link
Institution PBL Netherlands Environmental Assessment Agency (PBL), Netherlands,
Solution concept Partial equilibrium (price elastic demand)
Solution method Simulation
Anticipation Simulation modelling framework, without foresight. However, a simplified version of the energy/climate part of the model (called FAIR) can be run prior to running the framework to obtain data for climate policy simulations.


As a result of the growing world population and higher per capita consumption, production of food, feed, fibres and other products, such as bioenergy and timber, will need to increase rapidly in the coming decades. Even with the expected improvements in agricultural yields and efficiency, there will be increasing demand for more agricultural land. However, expansion of agricultural land will lead to deforestation and increases in greenhouse gas emissions, loss of biodiversity and ecosystem services, and nutrient imbalances. To reduce these environmental impacts, a further increase in agricultural yields is needed, together with other options such as reduced food losses, dietary changes, improved livestock systems, and better nutrient management.

In the IMAGE framework, future development of the agricultural economy can be calculated using the agro-economic model MAGNET (formerly LEITAP; Woltjer et al. (2011)1; Woltjer et al. (2014)2). MAGNET is a computable general equilibrium (CGE) model that is connected via a soft link to the core model of IMAGE. Demographic changes and rising incomes are the primary driving factors of the MAGNET model, and lead to increasing and changing demand for all commodities including agricultural commodities. In response to changing demand, agricultural production is increasing, and the model also takes into account changing prices of production factors, resource availability and technological progress. In MAGNET, agricultural production supplies domestic markets, and other countries and regions are supplied via international trade, depending on historical trade balances, competitiveness (relative price developments), transport costs and trade policies. MAGNET uses information from IMAGE on land availability and suitability, and on changes in crop yields due to climate change and agricultural expansion on inhomogeneous land areas. The results from MAGNET on production and endogenous yield (management factor) are used in IMAGE to calculate spatially explicit land-use change, and the environmental impacts on carbon, nutrient and water cycles, biodiversity, and climate.

MAGNET is connected via a soft link to the core model of IMAGE. The MAGNET model is based on the standard GTAP model 3, 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.

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 4, and dynamically depend on purchasing power parity 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 (129 regions). In linking with IMAGE, MAGNET distinguishes individual European countries and 22 large world regions, closely matching the regions in IMAGE (IMAGE regions). 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 5.

Land use

In addition to the standard GTAP model, MAGNET includes a dynamic landsupply function 6 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 76. In addition, MAGNET includes international and EU agricultural policies, such as production quota and export/import tariffs 8.


MAGNET distinguishes the livestock commodities of beef and other ruminant meats, dairy cattle (grass- and crop-fed), and a category of other animals (e.g., chickens and pigs) that are primarily crop fed. Modelling the livestock sector includes different feedstuffs, such as feed crops, co-products from biofuels (oil cakes from rapeseedbased biofuel, or distillers grain from wheat-based biofuels), and grass 1. 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. 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. 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. Recently, the earlier land supply curve 9 has been updated with a more detailed assessment of land resources and total land supply in IMAGE 10, and with literature data on current price elasticities. 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. In regions where most of the area suitable for agriculture is in use, the price elasticity of land supply is small, with little expansion occurring at high price changes. In contrast, in regions with a large reserve of suitable agricultural land, such as Sub-Saharan Africa and some regions in South America, the price elasticity of land supply is larger, with expansion of agricultural land occurring at smaller price changes.

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 11.

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 12, 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. 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.

Technology change in agriculture

The management factor (MF) describes the actual yield per crop group and per socio-economic region as a proportion of the maximum potential yield. This maximum potential yield is estimated taking into account inhomogeneous soil and climate data across grid cells. The MF for the period up to 2005 is estimated as part of the IMAGE calibration procedure, using FAO statistics on actual crop yields and crop areas 13. The start year for the MF is subsequently taken as point of departure for future projections.

Guidance for future development of yield changes is provided by expert projection such as the assumptions in FAO projections up to 2030 and 2050 1412.The FAO trends are used as exogenous technical development in the MAGNET model, and subsequently adjusted to reflect the relative shortage of suitable land, as part of the model calculation. The combinations of production volumes and land areas from MAGNET are adopted as future MF projections into the future in IMAGE.

Future technological change is dependent on the storyline and needs to be consistent with other scenario drivers. For instance, strong economic growth is typically facilitated by rapid technology development and deployment, rising wages and a labour shift from primary production (agriculture) to secondary (industry) and tertiary (services) sectors. These developments foster more advanced management and technology in agriculture. In order to reflect different trends in exogenous yield increase, FAO trends are combined with projections of economic growth to develop scenario-specific trends of yield changes in multiple-baseline studies, like for the SSPs. Because the MF is such a decisive factor in future net agricultural land area, careful consideration of uncertainties is warranted.


  1. a b  |  Woltjer GB, Kuiper M and van Meijl H (2011, Chapter 2: MAGNET.). . In 'The agricultural world in equations: An overview of the main models used at LEI. The Hague, Netherlands: LEI, part of Wageningen University and Research Centre.
  2. ^  |  Woltjer GB, Kuiper M, Kavallari A, van Meijl H, Powell J, Rutten M, Shutes L and Tabeau A (2014). The Magnet Model: Module description. The Hague, Netherlands: LEI, part of Wageningen University and Research Centre.
  3. ^  |  Thomas W. Hertel (1997). Global Trade Analysis. Cambridge University Press.
  4. ^  |  W Britz (2003). Major enhancements of @2030 Modelling system.Rheinische Friedrich-Wilhelms-Universität Bonn.
  5. ^  |  Paul S. Armington (1969). A Theory of Demand for Products Distinguished by Place of Production (Une theorie de la demande de produits differencies d'apres leur origine) (Una teoria de la demanda de productos distinguiendolos segun el lugar de produccion). Staff Papers - International Monetary Fund, 16 (), 159.
  6. a b  |  Hans van Meijl, T Van Rheenen, A Tabeau, B Eickhout (2006). The impact of different policy environments on agricultural land use in Europe. Agriculture, Ecosystems \& Environment, 114 (1), 21-38.
  7. ^  |  Jikun Huang, FW van Tongeren, Joe Dewbre, JCM van Meijl (2004). A new representation of agricultural production technology in GTAP. The Seventh Annual Conference on Global Economic Analysis, ().
  8. ^  |  J F M Helming (2010). European farming and post-2013 {CAP} measures: a quantitative impact assessment study. The Hague: LEI Wageningen UR.OCLC: 694730558. 
  9. ^  |  B. Eickhout, H. van Meijl, A. Tabeau, T. van Rheenen (2007). Economic and ecological consequences of four European land use scenarios. Land Use Policy, 24 (), 562-575.
  10. ^  |  Mandryk M.‚ Doelman J.C.‚ Stehfest E. (2015). Assessment of global land availability and suitability: land supply for agriculture. Wageningen, Netherlands: LEI.
  11. ^  |  Koen P Overmars, Elke Stehfest, Andrzej Tabeau, Hans van Meijl, A Mendoza Beltran, Tom Kram (2012). Estimating the costs of reducing CO2 emission via avoided deforestation with integrated assessment modeling. In Conference paper presented at the 15th Annual Conference on Global Economic Analysis(pp. 27-29). .
  12. a b  |  Nikos Alexandratos, J Bruinsma (2012). World agriculture towards 2030/2050: the 2012 revision (Publication No. ESA Working Paper No. 12-03) .Food and Agriculture Organization of the United Nations.
  13. ^  |  FAO (2013). FAOSTAT database collections. Rome,Italy: Food and Agriculture Organization of the United Nations.
  14. ^  |  J Bruinsma (2003). World agriculture: towards 2015/2030.. London: Earthscan.