Benchmarking Food Transformation in Africa Through the Use of Machine Learning Modelling

Abstract

Developing nations across the African continent face persistent challenges with food insecurity. In response to this, the CAADP framework has been adopted with the goal of transforming the agrifood sector in order to alleviate hunger. The objective of this study is to assess the extent to which various African nations have made progress towards reaching the targets of the CAADP framework and to identify indicators that can be used to inform food transformation policy. The GPT-4o Large Language Model (LLM) is employed to extract country profile data from the latest CAADP biennial report. While relevant macroeconomic indicators were obtained from publicly available World Bank and IMF databases. Several machine learning models are then employed to model and analyze the relationship between indicators and the C-value, a numeric value that represents a country’s progress towards a specific performance category. Initial results using a Random Forest Regressor model, developed to predict public expenditures to agriculture based on indicators such as government debt (% of GDP), government revenue (% of GDP), and government expenditure (% of GDP), show an r-squared value of 0.67 and a mean absolute error (MAE) of 0.875. Further models will be developed and evaluated using the most recent macroeconomic indicators from 2025 to provide a more nuanced assessment of the current status of food transformation in Africa.