Modelling of Post-COVID-19 Food Production Index in Nigeria using Box-Jenkins Methodology

Before the COVID-19 pandemic, global food security has been known to be a major threat for developed and developing countries of the world. However, during the COVID-19 pandemic, global food security was expected to be at a very high risk due to lockdown across the globe. Consequently, the developing countries, most especially, were expected to experience food shortage challenges. One important way to measure the amount of food production of any country in the world is through the use of a macroeconomic variable known as Food Production Index (FPI). Therefore, this study seeks to examine the post-COVID-19 behaviorof the Nigeria’s FPI using the Box-Jenkins methodology for modelingunivariate time series. A low-frequency time series datasets over 56 years spanning from 1961 to 2016 on Nigerian FPI was extracted from World Bank repository. Pre-tests results from the unit root analyses, correlogram and selection criteria techniques showed that the FPI is a differenced stationary series of order one {I(1)}and that ARIMA (2, 1, 2) model best fitted the series. Besides, diagnostic checking of the fitted model confirmed that the error was white noise and forecast of 8 years (2017 to 2024) was made. Findings from the study revealed that the future values of the FPI are erratic and expected to fluctuate (i.e.,rise and fall) within the predicted periods. Conclusively, the fourteen years out sample forecast of FPI for the periods 2017 to 2030 indicates that the gains of FPI in recent years is currently being affected bythe current COVID-19 pandemic. The study recommends that concerted efforts to achieve optimal FPI must be focused on the improvement of inter-regional trade which will result in shorter food chains, and thereby creating more market for farmers and enhancing accessibility to both inputs and outputs