Xin Dai

Amid mounting global food risks, the expansion of BRICS—an important multilateral platform for emerging markets and developing economies—has reshaped agricultural trade relations within BRICS, carrying profound implications for food security and sustainable agricultural development. This study aims to analyze the evolution of BRICS agricultural product trade networks to deliver empirical insights for coordinated agricultural governance among BRICS members. We adopt a social network analysis (SNA) and the quadratic assignment procedure (QAP) to examine BRICS agricultural product trade network characteristics and identify its driving factors. The results reveal that agricultural product trade within BRICS expanded markedly after BRICS enlargement, accompanied by declines in network density and reciprocity. Furthermore, nodes in this agricultural trade network show growing functional differentiation: Brazil serves as the core supply hub, China the primary demand hub, and Russia a vital connecting node. Community analysis indicates that the network has not evolved into closed geographic blocs; instead, cross-regional linkages give rise to a polycentric hierarchical structure. QAP estimates show that agricultural supply capacity dominated network configurations before enlargement, while food-security pressure and institutional distance exerted more pronounced impacts afterward. These findings provide empirical evidence and policy references for BRICS members to deepen agricultural trade cooperation, stabilize cross-border agricultural supply chains, and collectively safeguard food security.

Systemic risk in the global staple food trade network has become a critical threat to global food security amid escalating geopolitical frictions and frequent extreme weather events. This study constructs directed weighted trade networks for wheat, rice, maize and soybean covering 196 economies, and applies a cascading failure model to compare cross-crop structural vulnerability. Simulation results reveal stark heterogeneous risk profiles: a shock originating from a top rice exporter in India spreads across two cascading rounds and impacts 25.5% of total network nodes, while a parallel shock from a top soybean exporter in Brazil triggers nine propagation rounds affecting 86.2% of nodes; by comparison, equivalent shocks to wheat and maize networks only affect 22.4% and 24.5% of nodes respectively. Under identical targeted attack conditions, rice and soybean networks suffer sharp connectivity deterioration after removing a small number of core exporter nodes, while wheat and maize networks retain relatively higher residual connectivity under the same attack intensity, reflecting that crop trade systems with higher supply concentration exhibit greater systemic vulnerability. This work adopts a single-year static network framework; future research will extend to multi-year dynamic network tracking and incorporate adaptive supply-chain feedback mechanisms. Despite this boundary, the study fills the research gap in cross-crop systemic risk comparison, providing robust empirical evidence for differentiated global food supply-chain resilience governance.

Introduction: Driven by the dual forces of global climate change and sea-level rise, storm surge disasters have emerged as a primary external shock constraining the sustainable development of coastal socio-economic systems. Methods: Different from previous studies that mainly focus on average effects, this study uses panel data from 11 coastal provinces and municipalities in China from 2011 to 2023 and employs Tobit models, two-part models, panel threshold models, and fuzzy-set qualitative comparative analysis to examine the nonlinear characteristics and configurational pathways of direct economic losses from storm surges. Results: The results show that storm hazard intensity and coastal economic exposure are positively associated with direct economic losses from storm surges, whereas regional adaptive capacity shows a loss-mitigation effect. Direct economic losses exhibit evident nonlinear characteristics, and the loss-amplifying effect of storm hazard intensity increases once it exceeds the estimated thresholds. Low direct economic losses cannot be explained by any single necessary condition, but are associated with different combinations of conditions, mainly including Defense Strategy-Driven Pathway and Comprehensive Resilience-Driven Pathway. Discussion: These findings are of great significance for improving coastal disaster risk governance systems and enhancing differentiated disaster prevention and mitigation capacities.

As a major source of investment in Africa, the rapid growth of China’s Foreign Direct Investment (FDI) in Africa has exerted a profound influence on regional development and food security. Based on multinational panel data of African countries from 2006 to 2024, this paper systematically investigates the impact, transmission mechanisms, and heterogeneous characteristics of China’s FDI on food security in Africa. The empirical results show that China’s FDI in Africa has a significant positive effect on food security. Mechanism analysis indicates that China’s FDI improves food security indirectly, mainly through upgrading infrastructure and promoting agricultural technology spillovers. Moderating effect analysis reveals that a sound governance environment and strong absorptive capacity amplify its positive impact, whereas a less diversified industrial structure (a low share of secondary industry) weakens its effectiveness. This paper provides policy implications for optimizing the layout of China’s investment in Africa and promoting the sustainable development of Africa’s food system.

In the context of global warming, frequent agricultural droughts pose a severe threat to food security. Henan Province, as a primary maize-producing hub in China, is highly susceptible to these extreme climate events. Leveraging a comprehensive dataset combining county-level meteorological indicators, satellite remote sensing observations, and agricultural statistics spanning 2013 to 2022, this study investigated the stage-specific impacts of drought on maize yield and the mitigating role of irrigation investment. To identify the “critical windows” of crop vulnerability, we segmented the maize growth cycle into five distinct phases: sowing, emergence, jointing, tasseling, and maturity. Employing a two-way fixed effects model, the findings indicated that agricultural drought had a significant negative impact on yield. A one-unit increase in drought severity reduced output by 1.46 t/ha. The analysis revealed pronounced heterogeneity, identifying tasseling and maturity as the most vulnerable “critical windows,” where yield losses reached 0.79 t/ha and 0.78 t/ha, respectively. Crucially, interaction analysis demonstrated that irrigation served as vital “climate insurance,” significantly attenuating these marginal losses. Satellite remote sensing evidence via Normalized Difference Vegetation Index (NDVI) mediation analysis substantiated that irrigation maintains crop physiological activity and suppresses the transmission of drought shocks during sensitive phenological phases. These results provide a scientific basis for enhancing climate resilience through precision irrigation during reproductive stages, offering a crucial hydrological foundation for broad-scale agricultural strategies like the Integrated Soil-Crop System Management framework to stabilize grain production.

Accurate prediction of soybean futures prices is crucial for agricultural risk management and market decision-making. This study systematically evaluated nine state-of-the-art deep learning models—iTransformer, TFT, TCN, TimesNet, PatchTST, TiDE, TSMixer, SOFTS, and Time-LLM—for forecasting the DCE No. 2 soybean futures price, while also examining the impact of incorporating external market variables (USFP, ER, SF). The results demonstrate that in univariate forecasting, iTransformer achieved the lowest MAPE over short (5–10 days) and long (60 days) horizons, while TCN demonstrated the most stable performance at the medium-term horizon (30 days); TimesNet attained the best RMSE, making it more suitable for handling extreme volatility and controlling tail errors. Under multivariate settings (with the introduction of USFP, ER, and SF as exogenous variables), TFT demonstrates the best overall performance, significantly outperforming the LSTM baseline model across nearly all forecast horizons. The gains from exogenous variables depend on both the forecast horizon and the choice of covariates. These findings provide empirical guidance for participants in futures markets regarding model selection and covariate configuration, supporting more precise risk management and market decision-making.

In modern society with a highly developed Internet, online public opinions on swine epidemic diseases have become one of the important influencing factors for the fluctuation of pork prices. In this paper, the Baidu AI large model, Time-Varying Parameter-Stochastic Volatility-Vector Auto Regression (TVP-VAR) and Latent Dirichlet allocation (LDA) approaches are employed to investigate the dynamic impact of online public opinion regarding live swine epidemic diseases on fluctuations in pork price. The results show that: (1) Online public attention and negative sentiment exert significant time-varying impacts on pork price fluctuations, with these impacts being most pronounced in the short term and gradually diminishing over the medium and long term. (2) During the outbreaks of swine epidemic diseases, the impulse impact of online public attention and negative sentiment on pork price fluctuations exhibits distinct stage-specific characteristics. Initially, the impact is negative and subsequently turns positive before eventually waning. (3) The online discourse surrounding swine epidemic diseases can be categorized into four topics including disease transmission, vaccine technology, industry development, and disease prevention and control. Online public attention towards these four topics associated with negative sentiments generally contributes to variations in pork prices. Based on findings, several policy recommendations are proposed, including the timely release of swine epidemic disease information, the establishment and enhancement of the online public opinion monitoring and early warning system, as well as adherence to routine prevention and control of pig epidemic diseases.

Soybeans play a crucial role in global food security and international agricultural trade. As the world’s largest consumer and trader of soybeans, China faces significant external dependence on supply and concentration of import sources. In light of increasing uncertainties in the international political and economic landscape, risks within China’s soybean supply chain have become increasingly prominent, highlighting the need to explore the global soybean supply potential and optimize import strategies. In response to national food security strategic requirements and anticipated changes in global production capacity, the Global Agro-Ecological Zone (GAEZ) model and the multi-objective nonlinear programming model are used in this paper to estimate the potential of soybean yield increase globally. From dual perspectives of risk minimization and cost minimization, diversified soybean import schemes for China are proposed across three scopes: neighboring countries, countries along the Belt and Road Initiative (BRI), and globally. The results indicate that in the long term, the center of gravity for global soybean production capacity remains in the Americas; these areas, along with Europe and Africa, are key regions for China to expand its soybean import sources in the future. If all countries’ soybean production potentials are fully explored, China can achieve sufficient soybean supply by relying on neighboring countries as well as those countries participating in the BRI. Specifically, it is estimated that during the 2020s (2011–2040), the potential soybean production in the United States, Brazil, and Argentina could reach more than 290 million tons, 140 million tons, and 130 million tons, respectively, under scenarios of both yield increase and cultivated land expansion. Neighboring countries such as India and Russia also show significant potential, with India’s production potentially increasing by 42.8 million tons and Russia’s by 10.4 million tons. The results suggest that China can achieve a more balanced and secure import strategy by leveraging the production capabilities of countries in the Americas, Europe, and Africa while also fostering closer economic and agricultural cooperation with neighboring nations and BRI countries. Based on these findings, policy recommendations aimed at stabilizing and ensuring China’s soybean supply are discussed.

The 2008 melamine crisis led to widespread consumer distrust of Chinese domestic infant milk formula (IMF), which was substituted through a surge of imported products. Recent studies, however, suggest a revival in consumer confidence in Chinese domestic products and regulatory supervision. This coincides with a rise in consumer ethnocentrism and increased concern about foreign IMF, which arose during the COVID-19 pandemic. This study aims to investigate the multifaceted factors that influence consumers’ intention to purchase domestic IMF, using a structural equation model based on a survey of 563 online consumers. Our findings challenge conventional thinking that food safety governance, consumer ethnocentrism, and COVID-19 have no significant direct impact on IMF purchase decisions. Instead, consumer purchase intentions are driven more indirectly by mediating factors of perceived product quality and perceived behavioural control. The findings have significant implications for Chinese policymakers and industry stakeholders seeking to rebuild trust and strengthen the market position of domestic IMF in the post-melamine and post-pandemic era. By understanding the nuanced dynamics and consumer preferences in this market, international stakeholders can also develop more effective strategies to navigate and compete in the ever-evolving landscape of the Chinese IMF industry.