Assessing the Sensitivity of Global Maize Price to Regional Productions Using Statistical and Machine Learning Methods

Frontiers in Sustainable Food Systems

Thierry Brunelle¹  David Makowski²  Rotem Zelingher³ 

[1] CIRAD, UMR CIRED, Nogent-sur-Marne, France;Université Paris-Saclay, INRAE, AgroParisTech, Applied Mathematics and Computer Science (UMR 518), Paris, France;Université Paris-Saclay, INRAE, AgroParisTech, Economie Publique, Thiverval-Grignon, France;

关键词: food-security;    maize;    agricultural commodity prices;    regional productions;    machine learning;   

DOI  :  10.3389/fsufs.2021.655206

来源: DOAJ

【 摘 要 】

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 in the maize yield in Northern America can inflate the probability of maize price increase on the global scale. The maize productions in the other regions have a 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 in analyzing global prices of major commodities and reveal the strong sensitivity of maize prices to small variations of maize production in Northern America.

【 授权许可】

Unknown