<p>This paper assesses the effects of the adoption of site-specific recommendations generated through an Android app called RiceAdvice on rice farmers’ technological advantage and managerial performance in the Senegal River Valley (SRV). The study uses data collected through multi-stratified sampling procedures and comprises 1200 adopters and non-adopters of the app. Our approach involves addressing both selection bias and differences in production technologies to evaluate the causal impact of the app. Impacts are evaluated through a framework that couples recent selectivity-correction stochastic production frontier and metafrontier techniques with statistical matching. Based on these frontiers, the technical efficiency, technology gap ratio, and meta-technical efficiency are calculated as the bases for examining impacts. We found that production technologies are systematically different between adopters and non-adopters and the results noted the presence of selection bias, although only for adopters. The mean technology gap ratios are 94.5% for adopters and 76.6% for non-adopters, suggesting that relative to the latter, the former group produces approximately 18% more of the potential rice output associated with the best-practice technology. We estimate mean meta-technical efficiencies of 72.5% and 57.4% for adopters and non-adopters, respectively, which translate into a statistically significant managerial performance differential of approximately 15% points. Therefore, adoption of the RiceAdvice app enhances the production possibilities and managerial performance of rice farmers in the SRV. Efforts at mainstreaming the app in regular extension as well as reducing barriers to app access through a sustainable business model may help increase the impacts.</p>