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

Abstract

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.

An Extreme Learning Machine Approach for Forecasting the Wholesale Price Index of Food Products in India

“…ANN is flexible, applies universal approximators, supplies effective forecasting, and can operate on diverse time-series data, both linear and non-linear. The authors applied neural approach-based forecasting techniques to predict the future prices of various items, e.g., white beans (Sanusi et al, 2022), white maize (Sanusi et al, 2022), and soybean (Zhang et al, 2018). Some utilized ANN to forecast potato (Areef & Radha, 2020;Choudhury et al, 2019), coffee (Xu & Zhang, 2022b), and sugar (Xu & Zhang, 2022b) prices.…”

“…Some utilized ANN to forecast potato (Areef & Radha, 2020;Choudhury et al, 2019), coffee (Xu & Zhang, 2022b), and sugar (Xu & Zhang, 2022b) prices. A few employed ANN for price prediction of soybean oil (Xu & Zhang, 2022a;Xu & Zhang, 2022b), rice (Sanusi et al, 2022;Shao & Dai, 2018), and wheat (Shao & Dai, 2018).…”

“…Food price forecasting adds value to all stakeholders, e.g., policymakers, consumers, and agriculturalists, by providing reliable price projections of the food items (MacLachlan et al, 2022). Researchers applied eclectic time-series forecasting strategies to predict the prices of food items (Menculini et al, 2021;Mgale et al, 2021;Dacha et al, 2021;Sanusi et al, 2022;Mahto et al, 2021). The works of literature exhibited that the researchers utilized an assortment of techniques, e.g., Autoregressive Integrated Moving Average (ARIMA) by Adam (2022), Exponential Smoothing (ES) by Rosyid et al (2019), ʻError, Trend, Seasonality' (ETS) by Purohit et al (2021), Regression by Mishra et al (2019), and Artificial Intelligence (AI) by Sanusi et al (2022) to predict future prices of food items.…”

“…Researchers applied eclectic time-series forecasting strategies to predict the prices of food items (Menculini et al, 2021;Mgale et al, 2021;Dacha et al, 2021;Sanusi et al, 2022;Mahto et al, 2021). The works of literature exhibited that the researchers utilized an assortment of techniques, e.g., Autoregressive Integrated Moving Average (ARIMA) by Adam (2022), Exponential Smoothing (ES) by Rosyid et al (2019), ʻError, Trend, Seasonality' (ETS) by Purohit et al (2021), Regression by Mishra et al (2019), and Artificial Intelligence (AI) by Sanusi et al (2022) to predict future prices of food items. Whereas an array of approaches exists for food price forecasting, and none of them is a clear winner, it is imperative that accurate food price forecasting becomes critical for all stakeholders.…”

Determinants of Rice Farmers’ Revenue in North – Central, Nigeria

“…In Nigeria, rice is grown on a local scale in almost all agro-ecological zones (Fadairo et al, 2022). According to Sanusi et al, (2022), Nigeria is the world's largest importer of rice, the largest consumer of rice on the continent, and one of Africa's major producers of the grain. Since small-scale farmers typically sell 80% of their crop and consume only 20%, rice is a crucial commodity for food security and a crucial income crop (Danmaigoro & Gona, 2022).…”