Assessing the Sensitivity of Global Maize Price to Regional Productions Using Statistical and Machine Learning Methods - Zelingher, Rotem; Makowski, David; Brunelle, Thierry (Frontiers in Sustainable Food Systems, 2021)
Agricultural price shocks strongly affect farmers' income and food security. It is therefore important to understand and anticipate their origins and occurrence, particularly for the world's main agricultural commodities. In this study, we assess the impacts of yearly variations in regional maize productions and yields on global maize prices using several statistical and machine-learning (ML) methods. Our results show that, of all regions considered, Northern America is by far the most influential. More specifically, our models reveal that a yearly yield gain of +8% in Northern America negatively impacts the global maize price by about -7%, while a decrease of -0.1% is expected to increase global maize price by more than +7%. Our classification models show that a small decrease of the maize yield in Northern America is able to inflate the probability of maize price increase at the global scale. The maize productions in the other regions have much lower influence on the global price. Among the tested methods, random forest and gradient boosting perform better than linear models. Our results highlight the interest of ML for identifying global prices of major commodities and reveal the strong sensitivity of maize prices to small variations of maize production in Northern America.