Integrating Earth Observation data into the agricultural census: Methodological advances and global evidence

Abstract

The World Programme for the Census of Agriculture 2030 (WCA 2030) marks a paradigm shift in how countries design and implement agricultural censuses. It explicitly encourages the integration of Earth Observation (EO) and geospatial data to enhance efficiency, accuracy, and comparability. This paper presents a methodological synthesis of how EO can be embedded across the census cycle—from the preparation of geospatial reference layers and georeferencing of holdings to validation and area estimation. Drawing on lessons from FAO's EOSTAT programme, the UN Handbook of Remote Sensing for Agricultural Statistics, and innovative examples such as Brazilian Institute of Geography and Statistics's (IBGE) AI-based parcel delineation in Brazil, this article illustrates practical pathways for operationalization. The analysis emphasizes institutional readiness, quality assurance, and emerging AI-based approaches that enable scalable, cost-effective census operations aligned with WCA 2030 standards.