10th International Conference on Agricultural Statistics
08/07/2026 - 10/07/2026
10th International Conference on Agricultural Statistics
"Innovative Data Approaches: Shaping Resilient and Sustainable Agri-Food Systems in the Digital Age"
08 July 2026 - 10 July 2026
Kraków, Poland
Early bird registration by 17 March
The International Conference on Agricultural Statistics (ICAS) is a premier global forum dedicated to fostering and leveraging best practices, research, and innovation to meet the evolving needs of agricultural statistics. ICAS brings together producers and users of agricultural data, including national statistical offices, international agencies, academics, and private-sector actors, to share operational successes and explore the latest methodological developments.
Following the success of ICAS IX in Washington, DC, in 2023, which marked the 25th anniversary of the first ICAS, the 10th edition of the conference will be held from July 8 to 10, 2026, in Kraków, Poland. The conference will be hosted by Statistics Poland, in coordination with the Food and Agriculture Organization of the United Nations (FAO), the World Bank and the United States Department of Agriculture (USDA), and under the aegis of the Committee on Agricultural Statistics of the International Statistical Institute.
Scientific Programme
In this digital age, agricultural statistics must evolve to not only monitor change but to shape it. ICAS X invites participants to explore how data innovation can drive the transition toward more adaptive, productive, and environmentally responsible agri-food systems. ICAS X sub-themes are:
- Shocks, Risks and Resilience
- Food Security Diets and Nutrition
- Markets, Prices and Value chains
- Sustainable Agriculture Production & Consumption
- Natural Resources & Environmental Issues
- Digital Agriculture & AI
- Innovative Data Sources / Geospatial / Data Integration& Interoperability
- Capacity Building in Agricultural Statistics
- Data Access & Use for Policy-Making
- Data Production & Processing
- Rural Development, Poverty and Jobs
- Data Quality & Measurement Error