Abstract

Artificial intelligence (AI) offers opportunities to improve agricultural census operations, but adopting it requires evidence of statistical fitness, institutional accountability, and protection of respondent information. This paper proposes a practical framework for responsible AI across the census lifecycle within the World Programme for the Census of Agriculture 2030 (WCA 2030), covering the 2026–2035 round.

Drawing on WCA 2030 guidance, particularly paragraphs 5.53–5.54, and countries implementation experiences, the framework maps potential applications from questionnaire design, manuals and enumerator training to frame preparation, fieldwork monitoring, editing, coding, imputation and dissemination. It distinguishes conventional digital tools and rule-based checks from machine learning and generative AI, and separates operational applications from pilots and prospective uses. Each use case is assessed against its intended benefit, statistical risk, data requirements and need for human oversight.

The proposed controls include accountable statistical ownership, approved data access, confidentiality safeguards, documented model and data versions, audit trails, human review and fallback procedures. Validation combines comparison with established methods, independent test data and field verification, with checks for geographic and holding-level disparities. For classification and imputation, assessment extends beyond predictive accuracy to effects on coverage, bias, uncertainty and published aggregates. Generative outputs require verification against approved census concepts and source material before operational use or release.
Implementation choices are organised around country readiness, census modality, infrastructure, skills and total operating costs. A staged pathway progresses from bounded assistance with reviewed outputs to controlled pilots and production use, only when predefined quality and governance criteria are met. The contribution is a decision framework for national statistical offices to select, validate, scale or defer AI applications while maintaining WCA concepts, international comparability and public trust.