Land-use allocation/Description

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Model description of Land-use allocation

IMAGE 3.2 uses a regression-based suitability assessment to determine future land-use patterns. Land-use allocation is driven by regional crop and grassland production and their respective intensity levels, as calculated by the agro-economic model MAGNET (Agricultural economy). As the agro-economic model uses a different crop aggregation than IMAGE a specific mapping is used to convert seven MAGNET crop types to 16 IMAGE crop types. Agricultural land use is allocated to grid cells in an iterative process until the required regional production of crops and grass is met. Land use in IMAGE is modelled using dominant land use per grid cell on a 5 x 5 minute resolution, distinguishing extensive grasslands, agricultural and non-agricultural grid cells, and within agricultural land areas fractions of grass, seven rain-fed and seven irrigated crop types, and bioenergy crops.

In each time step, maps of actual crop yields are computed by combining the potential crop and grassland yields calculated by the crop model (Crops and grass), and the regional management intensity from the agro-economic model (Agricultural economy). Starting with the land-cover and land-use map of the previous time step, actual yields are used to determine crop and grassland production on current agricultural land. This is compared to the required regional crop and grassland production. If the demand exceeds calculated production, the agricultural area needs to be expanded at the cost of natural vegetation. If the calculated production of current cropland exceeds the required production, agricultural land is abandoned to adjust to the production required.

Crop and grassland is either abandoned or expanded until the required production is met. Since actual yields are taken into account, changes in crop yields in time due to technological change, climate change and land heterogeneity are included. If yields in the new agricultural areas are lower than average in the current area, relatively more agricultural land is required compared to the production increase.

In determining the location of agricultural expansion or abandonment, all grid cells are assessed and ranked using an empirically based suitability map. This map is developed using artificial neural network models that relate locations of agricultural land conversions, as recorded from 2003 to 2013, to various explanatory variables reflecting topography, climate, soil and accessibility (Cengic et al., 2020). Agricultural land conversion data is derived from the satellite-based ESA-CCI land cover database which provides land use transition data for the 1992-2018 period at 300 m resolution (ESA, 2017).

Additionally, a few other rules are applied in determining the location of new agricultural land. For instance, agricultural expansion is not permitted in protected areas, and in areas otherwise protected, such as in assumed REDD (reducing emissions from deforestation and degradation) schemes. A grid cell is only regarded suitable for agriculture if the potential rain-fed production is at least 10% of the global maximum attainable crop yield. Grid cells with a production potential between 0.01 and 10% of the maximum attainable are still assumed suitable for extensive grassland. An additional anthropogenic other land use class is excluded from agricultural land use expansion as this is assumed to be used for other purposes such as landscape aspects (roads, hedges, gardens), recreation (e.g. golf courses) or other human purposes than agriculture or urban land. This class is defined as the differences between anthropogenic land from the ESA-CCI land cover database and agicultural land as provided by the HYDE database.

Irrigated areas are increased on a regional scale, prescribed by external scenario dependent assumptions, such as based on FAO (Alexandratos and Bruinsma, 2012). In each time-step, more irrigated areas are allocated in agricultural land based on the need for irrigation (the difference in rain-fed and irrigated yields), and water availability.

In agricultural areas, the fraction of specific crops is determined based on the initial fractions, and modified annually based on changes in regional demand and local crop yields. As a result, the land-use fraction of a certain crop increases when the demand for this crop increases faster than for other crops, or if the potential yield in this grid cell increases more than for other crops.

The land use allocation model enables new land-use and land cover maps to be created (Land cover and land use). These land-use maps specify agricultural land, extensive grassland, and, land for sustainable bio-energy production. Crop fractions are allocated for 16 food and other non-energy crop types in IMAGE (wheat, rice, maize, tropical cereals, other temperate cereals, pulses, soybeans, temperate oil crops, tropical oil crops, temperate roots & tubers, tropical roots & tubers, sugar crops, palm oil, vegetables & fruits, other non-food, plant-based fibres, both rain-fed and irrigated), for grass and for five dedicated bio-energy crop types (sugar cane, maize, oil crops, wood biomass and grass biomass). These data are calculated on a 5 minute resolution, and aggregated to proportional land use on 30 minute resolution of the carbon, crop and water model LPJmL. As LPJmL uses a different set of crop types a specific mapping of IMAGE to LPJmL crop types is used (see table below).

Land use in IMAGE is modelled using dominant land use types per grid cell on a 5 x 5 minute resolution. In reality, land use is more heterogeneous. For some applications, dominant land use on 5 x 5 minute resolution, or the derived proportional land use on a 30 x 30 minute resolution may be sufficient. However, many applications require higher resolution and additional data, such as studies on biodiversity and agricultural intensification (Verburg et al., 2013).

Mapping used to translate IMAGE 3.2 crop types to crop model LPJmL crop types and agro-economic model MAGNET crop types (*demand for energy crops is determined by IMAGE-energy and not coupled to MAGNET (Bioenergy production))
Wheat Temperate cereals wheat
Rice Rice pdr (paddy rice)
Maize Maize grains
Tropical cereals Tropical cereals grains
Other temperate cereals Temperate cereals grains
Pulses Pulses v_f (veg, fruit, nuts)
Soybeans Soybeans oil_crops
Temperate oil crops Sunflower + Rapeseed (50%/50%) oil_crops
Tropical oil crops Groundnuts oil_crops
Temperate roots & tubers Temperate roots (sugarbeet) v_f (veg, fruit, nuts)
Tropical roots & tubers Tropical roots (cassave) v_f (veg, fruit, nuts)
Sugar crops Sugarcane (tropical) + Temperate roots (temperate) sugar crops
Oil, palm fruit Sugarcane oil_crops
Vegetables & fruits Others (grass) v_f (veg, fruit, nuts)
Other non-food, luxury, spices Others (grass) ocr
Plant based fibres Others (grass) pbf
Grains (biofuel) Maize IMAGE-energy*
Oil crops (biofuel) Rapeseed (temperate) + Groundnuts (tropical) IMAGE-energy*
Sugar crops (biofuel) Sugarcane (tropical) + Temperate roots (temperate) IMAGE-energy*
Wood biofuel Woody biomass IMAGE-energy*
Non-woody biofuel Grassy biomass IMAGE-energy*