The essential need to identify the most informative sources of greenhouse gas emissions (climate change drivers) impacting the food security nexus in Africa requires a comprehensive and holistic approach. Machine learning method excels in the identification of single-variable importance, our study complements their abilities by deriving principal components using principal component analysis (PCA) to give a significant understanding of the three primary greenhouse gases (carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O)). We used data from FAO concerning Africa from 2000 to 2021. Food household consumption emission (CH₄), burning crop residues (N₂O), and food transport (N₂O) are the key variables identified by machine learning as critical contributors to climate change drivers impacting food security. Biomass-burning emissions factor, land management emissions factor, and food supply chain emissions factor are the key principal components after subjecting the 86 to PCA. Focusing on the variables and the factors revealed by the extreme gradient, random forest, and PCA can help stakeholders develop efficient practices like promoting sustainable crop residue management and a sustainable food system that reduces post-harvest loss. Our study did not consider the potential impact of spatial effect in the identification of the key sources of greenhouse gases impacting food security, we will explore this in future works.
Bofa et al. (Tue,) studied this question.