Democratising Agricultural Commodity Price Forecasting: The AGRICAF Approach

Abstract

Ensuring food security is a critical global challenge, particularly for low-income countries where food prices impact the access to nutritious food. The volatility of global agricultural commodity (AC) prices exacerbates food insecurity, with international trade restrictions and market disruptions further complicating the situation. Despite online platforms for monitoring food prices, there is a significant gap in providing detailed explanations and forecasts accessible to non-specialists. To address this, we propose the Agricultural Commodity Analysis and Forecasts (AGRICAF) methodology, integrating explainable machine learning (XML) and econometric techniques to analyse and forecast global agricultural commodity prices up to one year ahead, dynamically adapting to different forecast horizons. This innovative integration allows us to model complex interactions and dynamics while providing clear, interpretable results. This paper demonstrates how AGRICAF can be used, applying it to three major agricultural commodities - maize, soybean, and wheat - and explaining how different factors impact prices across various months and forecast horizons. By facilitating access to accurate and interpretable medium-term forecasts of AC prices, AGRICAF can contribute to developing a fair and sustainable food system.