Forecasting impacts of Agricultural Production on Global Maize Price ; Prévision des impacts de la production agricole sur les prix mondiaux du maïs

Abstract

Agricultural price shocks strongly affect farmers' income and food security. It is therefore important to understand the origin of these shocks and anticipate their occurrence. In this study, we explore the possibility of predicting global prices of one of the world main agricultural commodity-maize-based on variations in regional production. We examine the performances of several machine-learning (ML) methods and compare them with a powerful time series model (TBATS) trained with 56 years of price data. Our results show that, out of nineteen regions, global maize prices are mostly influenced by Northern America. More specifically, small positive production changes relative to the previous year in Northern America negatively impact the world price while production of other regions have weak or no influence. We find that TBATS is the most accurate method for a forecast horizon of three months or less. For longer forecasting horizons, ML techniques based on bagging and gradient boosting perform better but require yearly input data on regional maize productions. Our results highlight the interest of ML for predicting global prices of major commodities and reveal the strong sensitivity of global maize price to small variations of maize production in Northern America.