AI-imputed and crowdsourced price data show strong agreement with traditional price surveys in data-scarce environments.

ABSTRACT: Continuous access to up-to-date food price data is crucial for monitoring food security and responding swiftly to emerging risks. However, in many food-insecure countries, price data is often delayed, lacks spatial detail, or is unavailable during crises when markets may become inaccessible, and rising prices can rapidly exacerbate hunger. Recent innovations, such as AI-driven data imputation and crowdsourcing, present new opportunities to generate continuous, localized price data. This paper evaluates the reliability of these approaches by comparing them to traditional enumerator-led data collection in northern Nigeria, a region affected by conflict, food insecurity, and data scarcity. The analysis examines crowdsourced prices for two staple food commodities, maize and rice, submitted dai

SUBMITTER: Adewopo J 

PROVIDER: S-EPMC11978078 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature