Proposal Description

The WCA 2030 guidelines explicitly place AI and machine learning within agricultural census modernization (Paragraphs 5.53 and 5.54) : they describe how AI/ML can automate labour-intensive tasks, improve questionnaire design, prepare manuals and training materials, support scalable digital training, optimize project management, monitor real-time progress and reduce errors, costs and delays. This IPS takes that guidance as its starting point and asks how such tools can be used responsibly in official agricultural census systems. It also links operational efficiency to the core statistical values of quality, transparency, comparability and trust, while keeping methodological accountability visible to users and producers.
The session will bring together FAO/WCA 2030 guidance, national census implementers and machine-learning experts to examine practical use cases across the census lifecycle: questionnaire design and testing; CAPI/CAWI, geotagging and paradata; fieldwork monitoring and adaptive follow-up; machine-learning checks for editing, coding and imputation; satellite, UAV and drone-based crop and livestock applications; and AI-ready dissemination through metadata-rich platforms and natural-language access.
The session addresses four statistical questions: which AI use cases are mature enough for census production; how outputs should be validated against statistical standards; what governance, security, confidentiality and human-review arrangements are required; and how countries with different capacity levels can adopt AI incrementally while preserving international comparability. The format combines five concise papers with a discussant reflection and an open exchange around a practical readiness framework.
The expected contribution is an implementation agenda rather than a technology showcase. Participants will leave with a clearer map of where AI can support planning, enumeration, processing, quality assurance, project management and dissemination; where traditional statistical controls remain essential; and how the WCA 2030 round can advance responsible, evidence-based modernization of agricultural census systems.