(3 years) lends credence to the innovativeness and ability to capture on-ground conditions that are represented by both approaches. The near-perfect agreement between the price data from both sources is indicative of a potential to couple human-centered price monitoring system with an alternate algorithm-dependent system to evolve a hybrid highfrequency price monitoring system, bolstered by inbuilt multi-temporal validation architecture. Specifically, crowdsourced data can feed into such envisioned system as georeferenced intraday price data points, providing spatially-rich stream of training and testing data into the AI algorithm pipeline, and potentially enhancing model performance and the credibility of data outputs. Conversely, the AI algorithm can be leveraged to generate relatively coarser price data and insights at multiple administrative levels across countries (or regionally), beyond the geographic coverage of crowdsourcing efforts, especially in contexts where crowdsourcing is ineffective or yields sparse data. The value of both innovative data methods transcends mere understanding of current or past trend of commodity prices. It is also a significant milestone towards forecasting emergent food crises based on the richness of data flow to test various assumptions about price movement, investment outlook, and overall resilience or vulnerability of households to shocks and stressors (Andrée, et al., 2020; Wang, et al., 2022; Gatti, et al., 2023).

Finally, the understanding that aggregation of prices from a variety of packaging units did not compromise the relative relationship between crowdsourced and AI-estimated prices can be useful to adapt future efforts to scale either of the innovative data curation methods. Generally, the AI-estimated prices are generated for wholesale package units i.e. 100kg packages, and represents wholesale prices (Andrée, 2021a). Meanwhile, the crowdsourced price data (at level 0) were submitted based on a variety of selling packages at farmgate, retail, and wholesale market segments, and the dataset contains information that differentiates the packaging units (Solano-Hermosilla, et al., 2021; Arbia, et al., 2023). Our results align with previous finding that price per unit of commodity (i.e. ₦/kg) is usually lower at wholesale segment than retail segment (Hirvonen, et al., 2021), however the correlative relationship between AI-estimated prices and the wholesale subset of the crowdsourced data remained strong ( _r_<sup>_2_</sup> =0.93). Realistically, markets in LMICs are predominantly characterized by sellers and buyers that transact commodities with different packaging units within or between stores or sheds. Therefore, to be agile and viable, price monitoring systems must be sufficiently robust to track prices across markets segments or diverse packaging units, while unraveling market signals based on price per unit, notwithstanding the source. In essence, the highly positive results from this analysis, without discrimination of the source market segments and or packaging units associated with the prices, indicate the plausibility of the crowdsourced and AI-estimated price data for overall market surveillance.

As both innovative approaches [co-]evolve, the findings from this study establish instructive evidence to support further development of these methods towards bridging extant data gaps in food price monitoring systems within LMICs, and contribute to advancement of datarich analytics, including scalable now-casting and forecasting of food insecurity over time and at regional, national, or sub-national levels (Andrée, 2022).

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