An inventory is required on the changing potentially suitable areas for agriculture under changing climate conditions. Within the context of the GLUES project, researchers at the Ludwig-Maximilians University (LMU) investigated the global agricultural suitability of land under changing climate conditions at high spatial resolution. The growing demand for food, feed, fiber and bioenergy increases pressure on land and causes land use/cover change and trade-offs between different uses of land and ecosystem services. In order to ensure food security, agricultural potentials need to be used more efficiently in the future. Therefore, the agricultural suitability of land are important information e.g. in order to identify todays suitable areas and possible future changes. The potential suitability of todays forested and protected areas can be used to identify possible hotspots of land use/cover change. Therefore, LMU is working on improving the knowledge of global agricultural potentials of land and better understanding the interdependencies between ecological and socio-economic systems which are driving land use/cover change.
Local climate, soil and topography determine the available energy, water and nutrient supply for agricultural crops and thus their natural suitability. In order to allow for computing the natural agricultural constraints on the globe at 30 arc seconds spatial resolution, the following high resolution data were applied:
Daily data for temperature, precipitation and solar radiation from the global climate model ECHAM5. Soil data comes from the Harmonized World Soil Database (HWSD). Considered soil properties are texture, proportion of coarse fragments and gypsum, base saturation, pH content, organic carbon content, salinity, sodicity. Topography data was applied from the Shuttle Radar Topography Mission (SRTM). Irrigation has strong impact on the crop’s suitability. It is considered on todays irrigated areas as given by the FAO Aquastat Global Maps of Irrigated Areas (GMIA) dataset. The determinant factors are contrasted with the crop-specific requirements, using a fuzzy-logic approach. The crop requirements are taken from literature.
Overview of the factors determining crop suitability
General agricultural suitability at a spatial resolution of 30 arcsec, considering rainfed conditions and irrigation on currently irrigated areas. The agricultural suitability represents for each pixel the maximum suitability value of the considered 16 plants. The dataset contains four time periods (1961-1990, 1981-2010, 2011-2040, 2071-2100).
Crop suitability for 16 crops at a spatial resolution of 30 arcsec, considering rainfed conditions and irrigation on currently irrigated areas. The dataset contains four time periods (1961-1990, 1981-2010, 2011-2040, 2071-2100). The considered crops are: Barley, Cassava, Groundnut, Maize, Millet, Oilpalm, Potatoe, Rapeseed, Rice, Rye, Sorghum, Soy, Sugarcane, Sunflower, Summer wheat, Winter wheat.
To download crop-specific data click on the desired crop name above.
Change in agricultural suitability and crop suitability due to climate change for SRES A1B scenario conditions for 16 crops between 1981-2010 and 2071-2100 at a spatial resolution of 30 arcsec.
Potential number of suitable crop cycles for 16 crops at a spatial resolution of 30 arcsec, considering rainfed conditions and irrigation on currently irrigated areas. The dataset contains four time periods (1961-1990, 1981-2010, 2011-2040, 2071-2100).
Start of the growing cycle for 16 crops at a spatial resolution of 30 arcsec, considering rainfed conditions and irrigation on currently irrigated areas. In case of multiple cropping, the start of the first growing cycle is shown. The dataset contains four time periods (1961-1990, 1981-2010, 2011-2040, 2071-2100).
|Detailled information are available in the following publication:
Zabel F., Putzenlechner B., Mauser W. (2014): Global agricultural land resources – a high resolution suitability evaluation and its perspectives until 2100 under climate change conditions. Online available: PLOS ONE. DOI: 10.1371/journal.pone.0107522
Analysis results are available as WMS service (GLUES WMS).
Information on datasets and web services are available in the GLUES Metadata catalog.
Please contact: Dr. Florian Zabel, email@example.com, Department für Geographie, LMU München (www.geografie.uni-muenchen.de)