Tracking Fertilizer Price Volatility: Expanding the Food Security Portal’s Excessive Food Price Variability Early Warning System

Fertilizer is a critical input for agricultural productivity, and its increased use has been closely associated with rising agricultural yields in many countries. In developing economies that rely heavily on fertilizer imports and are home to vulnerable smallholder farmers, fertilizer price spikes can pose serious risks. When farmers lack access to effective risk-sharing mechanisms, sudden or excessive increases in fertilizer prices can discourage adoption, disrupt planting decisions, and reduce productivity.

Understanding global fertilizer price dynamics is therefore key for informing timely policy interventions. Prices tend to be higher in more concentrated markets, both globally and locally. Moreover, the transmission of price volatility—defined as unusually large or frequent price swings— from international to domestic markets appears to be stronger than that of price levels. This underscores the importance of monitoring price volatility, particularly in international markets, to help policymakers mitigate potential disruptions in fertilizer use and, subsequently, rural livelihoods and well-being.

Since 2011, the Food Security Portal’s Excessive Food Price Variability Early Warning System (EWS) has identified periods of excessive price variability for staple commodities including maize, wheat, rice, soybeans, coffee, cocoa, sugar, cotton, and, more recently, energy products like crude oil and natural gas. The system relies on daily global market data, using a risk-based threshold known as the conditional value-at-risk (CVaR) to flag abnormal price behavior.

Recognizing the rising importance of fertilizer markets, the FSP has initiated efforts to expand the EWS to include key fertilizers: potash, urea, and di-ammonium phosphate (DAP). Unlike staple commodities or energy products, fertilizer prices are typically reported on a weekly basis. This lower data frequency presents analytical challenges, including smaller sample sizes and reduced granularity for detecting abnormal volatility periods. The FSP’s research has aimed to identify the most appropriate modeling approach for detecting excessive variability in fertilizer prices.

Methodological Approaches to Volatility Detection

Two methods were evaluated to identify abnormal price fluctuations in the fertilizer markets:

1. Martins-Filho et al. (2018) Two-Step Approach
   This method relies on a nonparametric approach to estimate high-order conditional quantiles (CVaR threshold) in two stages: a regression step followed by tail modeling. It is particularly effective with large, high-frequency datasets where detailed modeling of tail behavior is possible. However, it is computationally intensive and is less suitable for small-sample, low-frequency contexts like weekly fertilizer prices.
2. Yao and Hernandez (2025) Hill-Based Approach
   This method adapts the Martins-Filho framework by incorporating the Hill estimator: a simpler technique for estimating the tail index of heavy-tailed distributions. It requires fewer assumptions, is computationally efficient, and is more robust under data constraints, making it a strong candidate for monitoring fertilizer price behavior.

Key Findings: Comparing Methods for Potash, Urea, and DAP

Researchers compared the performance of these two methods using historical US Gulf weekly prices for potash, urea, and DAP. A key benchmark was how closely each model aligned with the theoretical expectation that roughly 5 percent of observations would exceed the estimated 95 percent CVaR threshold.

Across all three fertilizer products, the Hill-based method outperformed the two-step approach in identifying excessive volatility periods. For example, in the case of urea, the two-step method flagged 30.3 percent of weeks as exceeding the 95 percent threshold. This was far above the expected rate, suggesting an overestimation of excessive or unusual volatility periods. In contrast, the Hill-based method yielded an exceedance rate of 4.4 percent, closely aligning with the theoretical target.

These findings confirmed that the Hill-based approach was more reliable and better tailored for fertilizer prices reported on a weekly basis. Its ability to function effectively with small sample sizes positions it as a suitable tool for fertilizer-related early warning applications.

Conclusion

Expanding the Food Security Portal’s Excessive Food Price Variability Early Warning System (EWS) to include fertilizer prices is a critical step in strengthening food security monitoring. Among the methods tested, the Hill-based estimator proved to be the most suitable to monitor weekly fertilizer price data, offering robustness, efficiency, and ease of implementation.

The inclusion of urea, DAP, and potash in the system provides meaningful coverage of key global nitrogen, phosphorus, and potassium fertilizers, while additional fertilizer series can be readily included in the future as more data become available. This EWS expansion enhances its ability to detect early signs of market stress and inform responsive policy actions, particularly in developing countries where smallholder farmers remain highly vulnerable to input price shocks.

By integrating fertilizers into the early warning architecture, the Food Security Portal is advancing tools that help governments and development actors act swiftly to safeguard agricultural productivity and rural livelihoods in the face of recurrent global price fluctuations.

Manuel Hernandez isa Senior Research Fellow in IFPRI's Markets, Trade, and Institutions Unit  Feng Yao is a Professor and Chair in Economics at West Virginia University.  Soonho Kim is a  Senior Data Manager in IFPRI's Markets, Trade, and Institutions Unit.