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Abstract Extreme weather poses a major challenge to global food security by causing sharp drops in crop yield and supply. International crop trade can potentially alleviate such challenge by reallocating crop commodities. However, the influence of extreme weather stress and synchronous crop yield anomalies on trade linkages among countries remains unexplored. Here we use the international wheat trade network, develop two network-based covariates (i.e., difference in extreme weather stress and short-term synchrony of yield fluctuations between countries), and test specialized statistical and machine-learning methods. We find that countries with larger differences in extreme weather stress and synchronous yield variations tend to be trade partners and with higher trade volumes, even after controlling for factors conventionally implemented in international trade models (e.g., production level and trade agreement). These findings highlight the need to improve the current international trade network by considering the patterns of extreme weather stress and yield synchrony among countries.
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This content will become publicly available on May 21, 2026
Unveiling the drivers contributing to global wheat yield shocks through quantile regression
Sudden reductions in crop yield (i.e., yield shocks) severely disrupt the food supply, intensify food insecurity, depress farmers' welfare, and worsen a country's economic conditions. Here, we study the spatiotemporal patterns of wheat yield shocks, quantified by the lower quantiles of yield fluctuations, in 86 countries over 30 years. Furthermore, we assess the relationships between shocks and their key ecological and socioeconomic drivers using quantile regression based on statistical (linear quantile mixed model) and machine learning (quantile random forest) models. Using a panel dataset that captures spatiotemporal patterns of yield shocks and possible drivers in 86 countries, we find that the severity of yield shocks has been increasing globally since 1997. Moreover, our cross-validation exercise shows that quantile random forest outperforms the linear quantile regression model. Despite this performance difference, both models consistently reveal that the severity of shocks is associated with higher weather stress, nitrogen fertilizer application rate, and gross domestic product (GDP) per capita (a typical indicator for economic and technological advancement in a country). While the unexpected negative association between more severe wheat yield shocks and higher fertilizer application rate and GDP per capita does not imply a direct causal effect, they indicate that the advancement in wheat production has been primarily on achieving higher yields and less on lowering the possibility and magnitude of sharp yield reductions. Hence, in the context of growing extreme weather stress, there is a critical need to enhance the technology and management practices that mitigate yield shocks to improve the resilience of the world food systems.
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- Award ID(s):
- 2330502
- PAR ID:
- 10647351
- Publisher / Repository:
- Artificial Intelligence in Agriculture
- Date Published:
- Journal Name:
- Artificial Intelligence in Agriculture
- Volume:
- 15
- Issue:
- 3
- ISSN:
- 2589-7217
- Page Range / eLocation ID:
- 564 to 572
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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