Structural robustness of the international food supply network under external shocks and its determinants

2025-04-11

AI Summary

This study assesses the structural robustness of global staple grain (rice, wheat, maize, soybean) trade networks under multiple external shocks to inform food security governance. Method: We propose a novel robustness metric ( S(p,q) ) integrating shock breadth (( p )) and intensity (( q )), combining complex network analysis, maximum connected component evolution simulation, and random forest/XGBoost attribution modeling to characterize temporal resilience dynamics and identify vulnerable nodes. Contribution/Results: Over the past three decades, overall network robustness has increased, yet the system remains highly sensitive to extreme shocks affecting key exporters—particularly the U.S. and India. The soybean trade network exhibits the highest vulnerability. Rice and soybean production expansion significantly enhances global resilience, whereas rising grain prices markedly erode it. When ( p < 0.3 ) and ( q approx 1 ), ( S(p,q) ) decays linearly. These findings provide quantitative foundations for building resilience-oriented global food governance frameworks.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Natural Language Processing: Safety and Robustness
• Machine Learning: Adversarial Learning & Robustness

Application Category

• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

The stability of the global food supply network is critical for ensuring food security. This study constructs an aggregated international food supply network based on the trade data of four staple crops and evaluates its structural robustness through network integrity under accumulating external shocks. Network integrity is typically quantified in network science by the relative size of the largest connected component, and we propose a new robustness metric that incorporates both the broadness p and severity q of external shocks. Our findings reveal that the robustness of the network has gradually increased over the past decades, punctuated by temporary declines that can be explained by major historical events. While the aggregated network remains robust under moderate disruptions, extreme shocks targeting key suppliers such as the United States and India can trigger systemic collapse. When the shock broadness p is less than about 0.3 and the shock severity q is close to 1, the structural robustness curves S(p,q) decrease linearly with respect to the shock broadness p, suggesting that the most critical economies have relatively even influence on network integrity. Comparing the robustness curves of the four individual staple foods, we find that the soybean supply network is the least robust. Furthermore, regression and machine learning analyses show that increaseing food (particularly rice and soybean) production enhances network robustness, while rising food prices significantly weaken it.

Problem

• Evaluates structural robustness of global food supply network under shocks
• Proposes new metric combining shock broadness and severity effects
• Identifies key suppliers and factors influencing network resilience

Innovation

• Constructs aggregated international food supply network
• Proposes new robustness metric with broadness and severity
• Uses regression and machine learning analyses