1. Improving global agricultural cropland though integration and expert elicitation Steffen Fritz Linda See Christoph Perger

2. Activities of IIASA in the wider field of Early warning Global/regional land use models – impact of certain policies on GHG emissions, impact on food prices Crop modeling using crop modelling (e.g. Epic model), comparison of ASCAT Soil Moisture with EPIC soil moisture Analyzing the potential of mobile phones in cropland mapping and Early Warning applications

3. Current State of maps and possible improvements in data collection Global Land Cover and cropland maps in particular disagree Lots of national maps have been produced which can be integrated (e.g. Africover) Google earth offers enormous potential to cross check and validate maps Mobile phones allow to collect data – e.g. pictures on the ground

4. Disagreement between MODIS and GlobCover

5. Disagreement between GLC-2000 and MODIS

6. www.geo-wiki.org Geo-wiki.org

9. DatasetYear(s) Hybrid cropland extent map of Africa2000 to 2011 Hybrid cropland extent map of Africa Various temporal extents Africover for East African countries Map of cropland extent for Senegal 2000 2005 Cropland extent from the CORINE land cover dataset2005 Cropland extent for South Africa2010 Cropland extent for several states in India2010-2011 Map of cropland extent for Australia2010 Cropland extent for the USA 2010 Updated annually Cropland extent for China2000 Cropland extent for Southern Sudan2010 Cropland extent for Mali2007 Cropland extent for Nigeria2007 Cropland extent for Burkina Faso1983 Cropland extent for GambiaUnknown Crop masks for sugar cane and summer crops in Brazil 2010 Updated annually Cropland extent for one oblast in Kazakhstan2005 Data Shared at the Workshop

10. One Key Action from the Workshop IIASA leads new subtask under the GEO Agriculture task, and work with the GEO Agriculture ‘Community of Practice’, Subtask: ‘Global land-use map’ – Cropland irrigated/non-irrigated – Rangelands – Crop Type First step: Create a hybrid map of current cropland distribution.

11. How to make a ‘hybrid product’ Data integration – Kind of ensemble for cropland maps – Use national and sub-national statistics – Use experts via feedback on current maps to improve at certain locations – Integrate validation points collected from very high resolution (e.g. geo-wiki) and ground points/ pictures from mobile phones

12. Fritz et al., 2011, A new calibrated cropland dataset for sub-saharan Africa, JGR

15. Invitation to our Geo-wiki training session on: Improving African cropland using integration and expert elicitation Thursday afternoon 14.00-17.00 Prize: co-authorship