AI-Enabled Digital Connectivity and Agricultural Productivity in Remote Rural Saudi Arabia Under Vision 2030: Evidence from ARDL and NARDL

This study examines the relationship between AI-enabled digital connectivity and agricultural productivity in Saudi Arabia, with particular relevance to remote rural settings. Using annual time-series data covering 2000–2025, the study employs the Autoregressive Distributed Lag (ARDL) and Nonlinear ARDL (NARDL) approaches to investigate long-run relationships, short-run dynamics, and asymmetries in the productivity response to changes in digital connectivity. Because direct and consistent national-level measures of agricultural AI adoption are unavailable, mobile cellular subscriptions are used as a proxy for AI-enabling digital connectivity and the underlying digital infrastructure that facilitates technology-enabled agricultural applications. The ARDL bounds test confirms a stable long-run relationship between digital connectivity and agricultural productivity, with the long-run estimate indicating a positive and statistically significant, but moderate, association between connectivity and cereal yield. The short-run results reveal dynamic and delayed responses, consistent with adjustment processes associated with technological adoption and integration. The NARDL estimates further demonstrate statistically significant asymmetry, with the Wald tests rejecting symmetry in both the long run and short run. In particular, negative changes in digital connectivity generate a delayed adverse productivity response, highlighting potential vulnerability to disruptions in digital access. Overall, the findings indicate that digital connectivity functions as a complementary long-run productivity factor and that its benefits depend not only on expanding access but also on reliable infrastructure, technological capabilities, and supportive institutional conditions. The results underscore the importance of resilient and inclusive digitalization strategies for agricultural development in resource-constrained and remote rural environments.