Forecasting agricultural commodity price using different models: a case study of widely consumed grains in Nigeria

Purpose. This study highlights the specific and accurate methods for forecasting prices of commonly consumed grains or legumes in Nigeria based on data from January 2017 to June 2020. Methodology / approach. Different models that include autoregressive integrated moving average (ARIMA), artificial neural networks (ANN), seasonal decomposition of time series by loess method (STLM), and a combination of these three models (hybrid model) were proposed to forecast the sample grain price data. This study uses price data on widely consumed grains, such as white maize, local rice, imported rice, and white beans, in Nigeria from January 2017 to June 2020. Results. Our result indicates that ARIMA is the best applicable model for white maize and imported rice because it is well fitted to stationary data, as demonstrated in the sample period. The STLM is more appropriate in forecasting white beans. As white beans are highly seasonal in Nigeria, it further explains why the STLM model fits better in forecasting prices. The production of local rice is inconsistent in Nigeria because of erratic rainfall and stiff competition from the importation of rice from other countries. Therefore, and consistent with the analysis, the hybrid model is the best model applicable to local rice because it captures varying trends exhibited in the data. Originality / scientific novelty. This study suggests most accurate forecasting techniques for specific agricultural commodities in sub-Saharan African countries. It considers forecasting prices of commonly consumed grains and legumes in Nigeria and traded worldwide, such as imported rice, local rice, beans, and maize. Practical value / implications. The study highlights the importance of appropriate forecasts for policymakers, producers, and consumers to enhance better decision making and serve as an underlying incentive to guide the allocation of financial resources to the agricultural sector, which determines the structure and degree of sectoral growth.