IMAGEIntegrated Model to Assess the Global Environment.

Crops and grass


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Crop and grass module of LPJmL, in IMAGE 3.0
Flowchart Crops and grass. See also the Input/Output Table on the introduction page.

Key policy issues

  • How will climate change affect the productivity of current and future agricultural areas?
  • How could management improve agricultural productivity under current and future water constraints?
  • How will agriculture affect the Earth system with respect to carbon emissions, freshwater availability and nutrient cycles?


World population and per capita consumption of agricultural products are projected to increase substantially, which will require a significant increase in agricultural production. Currently, over one third of the Earth’s land area is under agricultural production, which is already about half the area suitable for agriculture. Pasture covers 68% of the global agricultural area, and cropland covers 32%. Agricultural production can be increased by expanding the agricultural area (more hectares) and by intensification (higher output per hectare).

However, the extent and distribution of agricultural land affects the Earth system, because agricultural systems are closely linked with natural ecosystems, human societies and the climate system. Agricultural land differs significantly from natural ecosystems in biogeochemical (e.g., carbon, water, nutrients) and bio-geophysical (e.g., albedo, energy balance) properties. Current land-use patterns have a significant impact on climate (Pitman et al., 2009; Strengers et al., 2010), and climate directly affects agricultural productivity (Müller et al., 2009; Rosenzweig et al., 2013). A large proportion of anthropogenic greenhouse gas emissions is caused by agricultural production, mediated by management and associated land-use dynamics (IPCC, 2007a).

Crop growth models are used to assess future area requirements, spatial patterns of agricultural production, and available areas for biomass-based energy (bioenergy). IMAGE 3.0 uses the LPJmL model on dynamic global vegetation, agriculture and hydrology (Bondeau et al., 2007; Fader et al., 2010; Waha et al., 2012). This model dynamically simulates plant growth, agricultural productivity, and the carbon and water dynamics of agricultural land with detailed processes of photosynthesis, respiration, growth and phenology. In the model’s current form, management intensity can be approximated per crop type on national scale (Fader et al., 2010). Irrigation patterns are obtained from the Land-use allocation model of IMAGE (Component Land-use allocation), and other management options are calculated internally, such as sowing dates, selection of crop varieties and the demand for irrigation water.

LPJmL simulates yields per crop under optimal management intensities for each grid cell and irrigation system as well as irrigation water requirements, which is input to the IMAGE Land-use allocation model (Component Land-use allocation) for simulations of land-use change dynamics. Climate change calculated by the IMAGE climate model (Component Atmospheric composition and climate) directly affects future agricultural productivity because these components are dynamically linked in annual time-steps.

Remark on Input/Output Table below: The LPJmL module on crop growth directly interacts with the modules on terrestrial carbon and water cycles; as they are all an integral part of the LPJmL model, sharing the same soil and water balance processes, the distinction in different modules is somewhat artificial.

Input/Output Table

Input Crops and grass component

IMAGE model drivers and variablesDescriptionSource
Cloudiness - grid (historical data) Percentage of cloudiness per month; assumed constant after the historical period CRU database
Number of wet days - grid (historical data) Number of days with a rain event, per month; assumed constant after the historical period CRU database
Change in soil properties - grid Change in soil properties, such as clay/sand content, organic carbon content, soil depth (topsoil/subsoil). Land degradation
Irrigation water supply - grid Water supplied to irrigated fields; equal to irrigation water withdrawal minus water lost during transport, depending on the conveyance efficiency. Water
Land cover, land use - grid Multi-dimensional map describing all aspects of land cover and land use per grid cell, such as type of natural vegetation, crop and grass fraction, crop management, fertiliser and manure input, livestock density. Land cover and land use
Management intensity crops Management intensity crops, expressing actual yield level compared to potential yield. While potential yield is calculated for each grid cell, this parameter is expressed at the regional level. This parameter is based on data and exogenous assumptions - current practice and technological change in agriculture - and is endogenously adapted in the agro-economic model. Agricultural economy
Precipitation - grid Monthly total precipitation. Atmospheric composition and climate
Temperature - grid Monthly average temperature. Atmospheric composition and climate
External datasetsDescriptionSource
Residue management Assumptions on residue management in agriculture.
Soil properties - grid Soil properties that have an effect on vegetation growth and hydrology. These characteristics differ between soil types. Relevant characteristics are soil texture and depth and water holding capacity HWSD database

Output Crops and grass component

IMAGE model variablesDescriptionUse
Crop irrigation water demand - grid Water requirements for crop irrigation, calculated as daily moisture deficit during the growing season.
Actual crop and grass production - grid Actual crop and grass production on agricultural land, based on potential yield and management intensityÍÍ
Potential crop and grass yield - grid Potential crop and grass yield, changing over time due to climate change and possibly soil degradation. In some components, i.e. 'Agricultural economy' regional aggregations of the dataset which depend on the actual land-use area, are used.
Potential bioenergy yield - grid Potential yields of bioenergy crops.
Irrigation water consumption - grid Irrigation water consumption by crops. Final output
Rainwater consumption - grid Rain water consumption by crops. Final output

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