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Violence and armed conflicts have emerged as prominent factors driving food crises. However, the extent of their impact remains largely unexplored. This paper provides an in-depth analysis of the impact of violent conflicts on food security in Africa. We performed a comprehensive correlation analysis using data from the Famine Early Warning Systems Network (FEWSNET) and the Armed Conflict Location Event Data (ACLED). Our results show that using conflict data to train machine learning models leads to a 1.5% increase in accuracy compared to models that do not incorporate conflict-related information. The key contribution of this study is the quantitative analysis of the impact of conflicts on food security predictions.

Reports from the Famine Early Warning Systems Network (FEWSNET) serve as the benchmark for food security predictions which is crucial for stakeholders in planning interventions and support people in need. This paper assesses the predictive accuracy of FEWSNET's food security forecasting, by comparing predictions to the following ground truth assessments at the administrative boundaries-livelihood level, revealing an overall high accuracy of 78\% across diverse timeframes and locations. However, our analysis also shows significant variations in performance across distinct regions and prediction periods. Therefore, our analysis sheds light on strengths, weaknesses, and areas for improvement in the context of food security predictions. The insights derived from this study not only enhance our understanding of FEWSNET's capabilities but also emphasize the importance of continuous refinement in forecasting methodologies.