Artificial Intelligence-Driven Integrated Water Management and Agricultural Sustainability: Evidence from Saudi Arabia

Water scarcity poses a critical challenge to sustainable agricultural development, particularly in arid regions such as Saudi Arabia. This study examines whether AI-compatible smart irrigation, digital water monitoring, and integrated water resource management (IWRM) are associated with improvements in agricultural water sustainability. Using a regional–crop panel dataset covering 13 Saudi administrative regions and six major crops over the period 2010–2024, the analysis evaluates their relationships with water-use efficiency, crop water productivity, and crop yield. To address persistence, endogeneity, and unobserved heterogeneity, the study employs a comprehensive multi-method empirical strategy combining dynamic panel models (System GMM), difference-in-differences, and event-study designs. The results provide internally consistent and empirically robust evidence in support of the proposed hypotheses. AI-compatible smart irrigation is positively and significantly associated with improvements in agricultural water efficiency and productivity, with effects that strengthen over time, reflecting gradual technology assimilation and learning processes. These findings capture the performance gains from irrigation modernization that enables data-driven and algorithm-supported decision-making, rather than the direct causal impact of autonomous artificial intelligence deployment. Integrated water resource management independently exhibits a positive association with higher agricultural performance, underscoring the importance of coordinated governance alongside technological adoption. Digital water monitoring shows a positive and statistically significant relationship with all outcome measures and appears to reinforce the effectiveness of both AI-compatible irrigation and integrated water governance. Robustness analyses excluding extreme drought years confirm that these relationships reflect persistent efficiency patterns rather than transitory climatic shocks. Overall, the findings provide context-specific and methodologically rigorous evidence that AI-compatible irrigation, digital monitoring, and integrated water governance operate as complementary components of agricultural water sustainability in a highly water-scarce economy, offering evidence-informed and policy-relevant insights, aligned with Saudi Vision 2030.