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Abstract Climate change is intensifying the frequency and severity of extreme events, posing challenges to food security. Corn, a staple crop for billions, is particularly vulnerable to heat stress, a primary driver of yield variability. While many studies have examined the climate impact on average corn yields, little attention has been given to the climate impact on production volatility. This study investigates the future volatility and risks associated with global corn supply under climate change, evaluating the potential benefits of two key adaptation strategies: irrigation and market integration. A statistical model is employed to estimate corn yield response to heat stress and utilize NEX-GDDP-CMIP6 climate data to project future production volatility and risks of substantial yield losses. Three metrics are introduced to quantify these risks: Sigma (σ), the standard deviation of year-on-year yield change, which reflects overall yield volatility; Rho (ρ), the risk of substantial loss, defined as the probability of yield falling below a critical threshold; and beta (β), a relative risk coefficient that captures the volatility of a region’s corn production compared to the globally integrated market. The analysis reveals a concerning trend of increasing year-on-year yield volatility (σ) across most regions and climate models. This volatility increase is significant for key corn-producing regions like Brazil and the United States. While irrigated corn production exhibits a smaller rise in volatility, suggesting irrigation as a potential buffer against climate change impacts, it is not a sustainable option as it can cause groundwater depletion. On the other hand, global market integration reduces overall volatility and market risks significantly with less sustainability concerns. These findings highlight the importance of a multidimensional approach to adaptation in the food sector. While irrigation can benefit individual farmers, promoting global market integration offers a broader solution for fostering resilience and sustainability across the entire food system.
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This content will become publicly available on April 1, 2026
Comprehensive Insights into Global Mineral Commodities: Analysis, Visualization, and Intelligent Assistance
With the growing emphasis on sustainability, criticality, and availability in materials research, providing actionable information about mineral commodities is crucial for informed decision-making and strategic planning by researchers, policy makers, and industry stakeholders. While the United States Geological Survey (USGS) offers valuable information on mineral-commodity summaries, their unstructured nature makes analysis challenging. To address this, we present a comprehensive data-analytics application () that processes the past 10 years of USGS mineral-commodity summaries into actionable insights. The application offers country-specific insights into global elemental production and reserves, along with quantitative metrics such as the Herfindahl-Hirschman index (HHI) to evaluate market concentration, identifying risks and opportunities in resource availability. It also features an artificial-intelligence assistant powered by a large language model (LLM) and a retrieval–augmented generation (RAG) system, enabling users to query various aspects of raw materials, including reserves, production, market share, usage, price, substitutes, recycling, and more. We evaluated multiple open-source LLMs for the RAG task and selected the best-performing model, , to implement in the system. This application provides valuable support for material scientists in assessing sustainability, criticality, and market risks, thereby aiding in the development of new materials. We demonstrate its application in energy materials, and by describing the application architecture and providing open access to the code, we aim to enable data-driven advancements in materials research.
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- Award ID(s):
- 2334411
- PAR ID:
- 10633570
- Publisher / Repository:
- PRX Energy
- Date Published:
- Journal Name:
- PRX Energy
- Volume:
- 4
- Issue:
- 2
- ISSN:
- 2768-5608
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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