Autoregressive Distributed Lag Modeling of Climate and Non-climatic Determinants Affecting Cereal Production: Empirical Evidence from Somalia

Abstract

This study investigates the impact of climate and non-climate factors on cereal production in Somalia from 1990 to 2019. The study used the Autoregressive Distributed Lag (ARDL) modeling approach to analyze the effect of CO2, temperature, rainfall, land under cereal production, and rural population on cereal production. The study employed the ARDL model to determine the long-run and short-run effects of the variables. The results indicate that all variables negatively impacted cereal production, except for land under cereal production, which had a positive effect on cereal production in both the short and long run. These findings suggest that policymakers should prioritize investment in land management practices to increase cereal production in Somalia. The study's results have important implications for stakeholders in the agricultural sector, highlighting the need for sustainable land use policies and climate change adaptation measures to ensure the country's food security.