This working paper synthesizes findings from four large household and community surveys in Myanmar, each covering a major agro-ecological zone, to evaluate inter-regional variations in the composition...
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.
Year of publication | |
Publisher | Frontiers in Sustainable Food Systems |
Geographic coverage | AmericaGlobal |
Knowledge service | Metadata | Global Food and Nutrition Security | Food security and food crises |FarmerFood price crisis |
Digital Europa Thesaurus (DET) | food securitymachine learningincomeagricultural production |
Key highlights
COVID-19 and other shocks In the 12 provinces surveyed, 61 percent of the interviewed agricultural households faced various forms of idiosyncratic and covariate shocks between...While a large body of literature on the impacts of agroforestry practices in LMICs is available, the social ecological impacts of agroforestry interventions is less well studied. This knowledge gap on the effectiveness...