Forecasting impacts of Agricultural Production on Global Maize Price

Prévision des impacts de la production agricole sur les prix mondiaux du maïs

Rotem Zelingher (), David Makowski () and Thierry Brunelle ()
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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: 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 nationale des ponts et chaussées - Université Paris-Saclay - CNRS - Centre National de la Recherche Scientifique, Agronomie - 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 nationale des ponts et chaussées - Université Paris-Saclay - CNRS - Centre National de la Recherche Scientifique, Cirad-ES - Département Environnements et Sociétés - Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement

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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.

Keywords: Food-security; Maize; Agricultural commodity prices; Regional production; Machine learning; Sécurité Alimentaire; Maïs-Cultures; Prix agricole; Production agricole et alimentaire localisée (search for similar items in EconPapers)
Date: 2020-09-22
New Economics Papers: this item is included in nep-agr and nep-big
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