Is digital transformation the key to agricultural strength? A novel approach to productivity and supply chain resilience

• The study underscores the pivotal role of digital technology adoption in modernizing agricultural practices and enhancing supply chain resilience. • By employing SEM, the researchers comprehensively examined the intricate relationships between the adoption of digital technology, agricultural infrastructure, market access, government support, environmental factors, agricultural productivity, and supply chain resilience. • The findings reveal that adoption rates of digital technology significantly influence agricultural productivity, which, in turn, positively impacts market access and supply chain resilience. • The study emphasizes the crucial role of government support and favorable environmental conditions in bolstering supply chain resilience. • The analysis highlights regional variations in technology adoption rates, farm sizes, and production levels, suggesting the need for tailored digital interventions to optimize agricultural practices and enhance supply chain resilience. This study addresses the critical gap in understanding how digital technology adoption influences supply chain resilience and agricultural productivity within China's evolving agrarian sector. While digital technologies promise transformative benefits, their specific impacts on key factors such as infrastructure, market access, government policies, and environmental conditions remain underexplored. Our research aims to uncover the interrelationships between Digital Technology Adoption Rates (DTAR), Agricultural Infrastructure (AI), Market Access (MA), Government Support (GS), and Environmental Factors (EF) to provide actionable insights for enhancing Agricultural Productivity (AP) and Supply Chain Resilience (SCR) in regional contexts. We employed SEM path analysis modeling to evaluate data from agricultural stakeholders across five Chinese provinces (Shaanxi, Sichuan, Anhui, Hubei, and Hunan), representing diverse age groups, farm sizes, and practices. The model assessed hypotheses linking DTAR, AI, MA, GS, and EF to AP and SCR, enabling a systematic assessment of direct and indirect effects. DTAR and AI emerged as pivotal drivers of AP (68% variance explained), which subsequently strengthened MA (73% variance) and SCR (62% variance). GS and EF were also critical, directly enhancing SCR and underscoring the role of policy and environmental stability. Regional disparities were evident: Shaanxi (24.95%), Sichuan (20.79%), and Hubei (19.75%) exhibited higher contributions, attributed to varying technology adoption rates, farm sizes, and production scales, signaling the need for region-specific digital strategies. This study is among the first to apply SEM in analyzing digital agriculture's role in supply chain resilience, offering a holistic framework that integrates technological, infrastructural, policy, and environmental dimensions. It advances theory by elucidating how digital integration mediates productivity and resilience, while practical contributions include targeted recommendations for scaling digital infrastructure and tailoring policies to regional conditions. Policymakers and stakeholders can leverage these insights to fortify supply chains against global and local disruptions, fostering sustainable agricultural transformation.