Digital technology and grain industrial chain resilience: mechanisms, heterogeneity, and threshold effects

Introduction Against the backdrop of rising global food system vulnerabilities driven by climate shocks, geopolitical disruptions and pandemic aftershocks, enhancing grain industrial chain resilience has become a strategic priority for countries worldwide. Existing literature has widely documented the productivity gains of digital technology in agriculture, yet the causal pathways and boundary conditions through which digitalization shapes grain industrial chain resilience remain under-explored. Methods Drawing on panel data for 285 prefecture-level cities in China spanning 2014 to 2024, this study develops a composite digital technology index and assesses the resilience of the grain industrial chain across three dimensions grounded in resilience theory: resistance, recovery, and transformation capacity. Employing a two-way fixed effects model, instrumental variable estimation, panel threshold model and mediated effects framework, we examine the causal impact, transmission mechanisms and heterogeneous patterns of digital technology on grain industrial chain resilience. Results The results indicate that digital technology exerts a significant positive effect on grain industrial chain resilience, and this finding remains robust after endogeneity adjustment and multiple robustness checks. Mechanism analysis demonstrates that digital technology enhances resilience through two complementary channels: improving resource allocation efficiency and fostering industrial synergy and integration. Heterogeneity analysis shows that the resilience-enhancing effect of digital technology is stronger in eastern regions and plain areas, while dimensional differences appear only as a point-estimate pattern and lack statistical verification. Threshold tests confirm two single-threshold effects, with digital infrastructure and farmers' digital literacy serving as boundary conditions. Further dimensional analysis shows a differentiated constraint pattern. Discussion This study provides empirical evidence and targeted policy references for digital transformation in the grain sector.