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Assessing the Sensitivity of Global Maize Price to Regional Productions Using Statistical and Machine Learning Methods
[Évaluation de la sensibilité du prix mondial du maïs aux productions régionales à l'aide de méthodes statistiques et d'apprentissage automatique]

Author

Listed:
  • Rotem Zelingher

    (ECO-PUB - Economie Publique - AgroParisTech - Université Paris-Saclay - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

  • David Makowski

    (MIA Paris-Saclay - Mathématiques et Informatique Appliquées - AgroParisTech - Université Paris-Saclay - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

  • Thierry Brunelle

    (CIRED - Centre International de Recherche sur l'Environnement et le Développement - Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement - EHESS - École des hautes études en sciences sociales - AgroParisTech - ENPC - École des Ponts ParisTech - Université Paris-Saclay - CNRS - Centre National de la Recherche Scientifique)

Abstract
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.

Suggested Citation

  • Rotem Zelingher & David Makowski & Thierry Brunelle, 2021. "Assessing the Sensitivity of Global Maize Price to Regional Productions Using Statistical and Machine Learning Methods [Évaluation de la sensibilité du prix mondial du maïs aux productions régional," Post-Print hal-03253794, HAL.
    • Handle: RePEc:hal:journl:hal-03253794
    DOI: 10.3389/fsufs.2021.655206
    Note: View the original document on HAL open archive server: https://hal.inrae.fr/hal-03253794
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    References listed on IDEAS

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    Cited by:

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