# **Introduction**

Food insecurity poses a lingering threat to national development across low- and middleincome countries (LMICs), and the recent inflationary trend has severely undercut food affordability for millions of households (World Bank, 2023; Headey & Ruel, 2023). The recent Shared Prosperity Report (World Bank, 2022) reveals that globally, 658 million individuals live below the international poverty line of $2.15 per day. Furthermore, estimates suggest that between 690 million and 783 million people worldwide experienced hunger in 2022 (FAO, IFAD, UNICEF, WFP and WHO, 2023; FSIN and Global Network Against Food Crises, 2023). Among these, over 258 million are in a food crisis, and forced to meet minimum dietary needs by employing irreversible coping strategies, including the liquidation of livelihood assets (FSIN and Global Network Against Food Crises, 2023). The international community's response to the global food crisis has encompassed significant humanitarian aid to directly protect livelihoods, as well as increased investment in resilience and prevention. Transitioning from reactive to proactive aid necessitates rigorous monitoring to gather information for early detection and to inform responses to emerging food security risks (Lentz & Maxwell, 2022).

Food and consumer price inflation significantly impact household food affordability, and both are leading indicators of food insecurity (Andrée, 2021b; Easterly & Fischer, 2021; Waterlander, et al., 2019). Generally, food price data reflect market pulse and sentiment, and capture reactions to shocks and threats. Monitoring of commodity prices and food price inflation is an important tool in early warning and rapid assessment of risks to markets and food security (Baquedano, 2015; Andrée, 2021a). However, regional or national system support for frequent tracking of changes in food prices at various market segments is rare in LMICs (Adewopo, et al., 2021; Zeug, et al., 2017; Galtier, et al., 2014).

For ex-ante and ex-post assessment of food security and market functioning, it is crucial to monitor food prices at sub-national levels, especially in fragile contexts where majority of households are living at the brink of poverty, and limited capacity or opportunity exists for local safety nets. In most LMICs, price monitoring and reporting systems for major staple commodities are rife with various intractable challenges, including planning time, costs, and logistical constraints to collect data at relevant granularity (i.e. market segments, commodity sub-types, spatial and temporal scales, etc.) to derive national and sub-national intelligence on market or price dynamics (FAO, 2017; Galtier, et al., 2014). Yet, governmental institutions, development agencies, and market stakeholders rely on such intelligence to make decisions about appropriate development and humanitarian interventions or investments. The underlying commodity price data are typically generated by designated national agencies or third-party institutions, through snapshot surveys of markets or implementation of arguably non-transparent proxy methods (Green, et al., 2013; Kalkuhl, et al., 2016). Proper food system planning and appropriate intervention require robust data and credible insights for time-relevant, targeted action, and reflexive impact assessment. At best, current official data represent a lagging rear-view of food security status and changes, and at worst, they misrepresent or under-represent the critical nuances of market signals in space and time, thereby eroding the confidence of decision-makers and data users.

Within the past decade, researchers have proposed and tested various innovative approaches to bridge the food price data gap in fragile contexts, producing mixed results (Kisan, 2015; Zeug, et al., 2017). The inherent applicability of these approaches can be adjudged relative to core elements of data innovation, including replicability, synchronicity, validity, and velocity over time and space. Data crowdsourcing and artificial intelligence (AI) are two major rapidly emerging, and independently evolving, approaches that are 2