Modelling and Forecasting Wholesale Potato Prices in Northern India Using SARIMA

Abstract

Farmers face many difficulties as a result of price fluctuation in agricultural commodities, mostly in developing nations like India. Potato prices are particularly unstable during the post-harvest period, which frequently forces farmers to sell at low prices because of urgent financial needs and delayed market information. The objective of this study is to use the Seasonal Autoregressive Integrated Moving Average (SARIMA) model to forecast monthly wholesale potato prices. Three important markets in Northern India i.e. Uttar Pradesh, a significant producer, Punjab, a distribution hub, and Delhi, a major consumption center were studied for price trends. The AGMARKNET portal was used to collect monthly wholesale price data from January 2010 to December 2024. The best fitted SARIMA models were determined using the lowest AIC and BIC values: SARIMA(2,0,0)(2,0,1)[12] for Uttar Pradesh, SARIMA(1,0,1)(1,1,1)[12] for Punjab, and SARIMA(1,0,1)(0,1,1)[12] for Delhi. Forecast results reveal clear seasonal patterns. Prices in Uttar Pradesh are expected to decline from Rs. 1986.61 in January to a low of Rs. 1629.92 in April, before rising again to Rs. 1821.96 in July. Similarly, the lowest forecasted prices are observed in March and April in Punjab (Rs. 1546.22) and Delhi (Rs. 1664.95), while the highest price is projected for October in Delhi (Rs. 2039.61). The observed patterns suggest that the post-harvest months, specifically from February to April, typically see a decline in prices attributed to market saturation. Conversely, prices tend to increase during the mid to late year period, likely influenced by a decrease in fresh arrivals and a heightened dependence on stored produce. The forecast emphasizes the importance of market-specific dynamics and illustrates the effectiveness of predictive models in assisting farmers marketing decisions. This enables improved planning by traders and policymakers to address seasonal price volatility. &nbsp