Seasonal Variations And Forecasting In Wholesale Prices Of Okra In Surat Market Of Gujarat, India

Abstract

The analysis of prices and market arrivals over time is important for formulating a sound agricultural price policy. Fluctuations in market arrivals largely contribute to price instability. In order to devise the appropriate ways and means for reducing the price fluctuations of agricultural commodities, there is a need to have a thorough understanding of the price behavior over time.Forecasting the price of agricultural commodities, presents some unique challenges such as data quality issues, weather aberrations, high fluctuations, price variations across neighboring marketplaces, etc. On the demand side, the instability in the prices of agricultural commodities is influenced by a number of factors such as annual variation in production, low price elasticity of demand and seasonality of agricultural production.The study relied upon the secondary time series data on monthly market prices and arrivals of Okra collected from the Agricultural Produce Market Committee, Surat. Analysis was performed on the monthly and annual time series data on wholesale price to develop reliable forecast for 2022. Month wise Seasonal indices of Okra in the Surat market showed that the seasonal price indices were above average from November to March while below average from May to October when market arrivals are more. The seasonal pattern showed that prices declined from April to October and reached the lowest point in May and reached peak in December. Seasonal fluctuations were observed both in market arrivals as well as prices of Okra. In the present investigation, various Seasonal, Non-seasonal, and Seasonal ARIMA (Box-Jenkins) models were developed to measure the forecast accuracy. The best model was chosen on the basis of the least values of Schwarz Bayesian criteria (SBC) and Mean absolute percentage error (MAPE). After performing a series of diagnostic tests, it was observed that N-BIC (12.417) and MAPE (22.88) were the least for SARIMA (0,0,2) (0,1,1)12 model. It came out to be the most representative model for the price of Okra in the Surat market. The model can be used for reaching dependable price forecasts. The quantification of these aspects in the vegetables is an immediate need to formulate effective policies to make prices stable thereby safeguard the interest of the farmers as well as the consumer.