Shocks in agricultural production: origins and impacts on commodity prices

Chocs dans la production agricole: origines et impacts sur les prix des produits de base

David Makowski (), Rotem Zelingher () and Christophe Gouel
Additional contact information
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
Rotem Zelingher: UMR PSAE - Paris-Saclay Applied Economics - AgroParisTech - Université Paris-Saclay - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement

Post-Print from HAL

Abstract: Price instability is recognized as a major driver of food insecurity that may have negative impacts on the social, economic and health status of many households. Since the food price spikes of 2007–8, prices of the main food and feed crops have shown high variability in the international and local markets for almost a decade. Although agricultural commodity prices are influenced by many factors, shocks on agricultural productions are often recognized as a major driver of price volatility. Adverse environmental conditions and the resulting decline in regional production have be shown to have greatly contributed to spikes in global food prices. The connection between production levels, agricultural commodity prices and food security has led many companies, governments and international organizations to develop forecasting systems to anticipate production shortages or, on the contrary, overproduction. The last decade has seen a rising number of applications of machine learning, with a particular focus on anticipating agricultural production shocks. The reasons are multiple. First, machine learning techniques can now be more easily applied than ever before using specialized packages implemented in open source packages. Second, large agricultural databases are becoming increasingly easily accessible, thanks in large part to the emergence of good practices in open science. Finally, mechanistic models were found to miss several major yield losses in Europe and in the US which led scientists to look for alternative solutions. For all these reasons, different machine learning tools are now being used more often to analyse production shocks, identify their origins, and assess their consequences on food security. Based on a brief review of the literature, we identify the main biotic and abiotic factors determining crop production shocks, propose a hierarchy for the factors with the most impact, and show that these factors are often interdependent, making crop yield forecasting rather difficult. Next, based on our own recent research projects, we show how the use of machine learning and of probabilistic models could open – in connection with the development of open access databases and new powerful algorithms – a new avenue for predicting production shocks and their impacts on agricultural prices. Lastly, we discuss the economic mechanisms that link together seasonal production forecasts and market impacts and how improving production forecasts could reduce price volatility and the risk of abrupt shifts in price trends.

Keywords: commodity prices; agricultural production; shocks; machine learning (search for similar items in EconPapers)
Date: 2022-06
References: Add references at CitEc
Citations:

Published in Sustainable Food Systems for Food Security: Need for Combination of Local and Global Approaches, Quae, 2022, 9782759235759

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03725864

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().