that new data approaches meet minimum requirements to establish their validity and dependability, relative to extant conventional approaches.

Since FPCA was implemented in two (2) discontinuous phases, there were two short gaps (4months and 6 months) in the temporal trend of the crowdsourced data within the 3-year period (Figure 3a), yet the data trajectory reflects major shifts in prices. This comparative assessment provides compelling evidence that suggests both validity and complementarity of crowdsourced and AI-estimated price data under demonstrably limiting geographic context. At the first level, the magnitude of agreement between the prices that were curated through both data sources transcended initial expectation. As new efforts emerge to apply crowdsourcing approach to generate high-frequency and large volume of data (Manners, et al., 2022; Minet, et al., 2017), there remains a lingering and prevalent notion that citizen volunteers are unlikely (or incapable) to submit credible data that can be as reliable as data from trained enumerators (Zeug, et al., 2017). During the period, total of 2,355 and 102,842 individual price datapoints accrued to ground truth enumerators and volunteer crowd, respectively, within the focal region. Both datasets were originally conveyed with different pre-defined local packaging units and market segments, however majority of the datapoints (72%) were submitted from retail-based market sources. The strong agreement between both price dataset obviates doubts regarding the usefulness and validity of high-frequency data sourced directly from citizens or market actors.

Beyond the previously highlighted limitations of new generation data imputation methods, AI-based algorithms can be constrained by paucity of training data to consistently validate model outputs. Due to limited availability of food price data for model calibration in fragile contexts, it is within reason to assume inherent unstable model behaviors for northern region of Nigeria, subject to intrinsic model robustness, which may lead to imputation of egregious price data outputs, especially over a long timeframe, characterized by multiple seasons. Note that the period was ostensibly defined by a market-disruptive shock of calamitous proportions, the COVID-19 pandemic, which triggered unanticipated episodic price behavior. The market price effects during this period were duly reflected in the trend of crowdsourced data, as previously reported (Adewopo, et al., 2021), and the strong correlation with AI-estimate prices suggest that the underlying algorithm effectively modeled both the periodic shifts and the indicative monthly changes in prices.

It is noteworthy that the inherent price signals in crowdsourced data became more observable as multiple daily submissions were averaged into daily, weekly, and monthly values. This is attributable to the tendency of averaged price values to coalesce around the modal values, thereby minimizing the effect of outliers and amplifying the signal-to-noise ratio. The emergence of observable price signal from the plume of crowdsourced datapoints can be valuable for various purposes in food system analysis and decision-support. At the basic level, the detectability of price signals addresses a major need to track seasonal price dynamics, and can support the development of agile analytical systems that links commodity prices to ancillary socio-economic and ecological factors. Unequivocally, the temporal granularity of commodity price monitoring systems should match the relevant temporal scale of target market-focused policies or food systems intervention. Therefore, the highfrequency datapoints proffer the possibility to aggregate price data at the required timescale for data-driven insights and decision-making in fragile national or regional contexts.

The monthly AI-based estimates are currently generated across 36 countries, with comparable survey data available from different organizations to potentially scale up such approaches in other countries. The strong correlation between AI-estimated prices and crowdsourced data, triangulated against enumerator-led data, over an extended timeframe 12