Modelling and Forecasting Wholesale Potato Prices in Northern India Using SARIMA

Lovepreet Singh; Surbhi Bansal; Ayaz Khan Nasiri

doi:10.62810/jnsr.v3i4.340

Authors

Lovepreet Singh Department of Agricultural Economics, Faculty of Agriculture, Guru Kashi University, Punjab, India
Surbhi Bansal Department of Economics, Kamla Lohtia Sanatan Dharam College, Punjab, India
Ayaz Khan Nasiri Department of Agricultural Economics and Extension, Faculty of Agriculture, Kabul University, Kabul, Afghanistan

DOI:

https://doi.org/10.62810/jnsr.v3i4.340

Keywords:

Forecasting, Potato Price, SARIMA model, Seasonal Trend, Stationarity

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.

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References

Anjoy, P., Paul, R. K., Sinha, K., Paul, A. K., & Ray, M. (2017). A hybrid wavelet based neural networks model for predicting monthly WPI of pulses in India. Indian Journal of Agricultural Science, 87(6), 834-839. https://doi.org/10.56093/ijas.v87i6.71022

Badal, P. S., Kamalvanshi, V., Goyal, A., Kumar, P., & Mondal, B. (2022). Forecasting potato prices: Application of ARIMA model. Economic Affairs, 67(4), 491–496. https://doi.org/10.13140/RG.2.2.12974.48966

Box, G.E.P. & Jenkins, G.M. (1976). Time Series Analysis: Forecasting and Control, Revised Edition, San Francisco: Holden Day. ISBN 0816211043.

Divisekara, R. W., Jayasinghe, G. J. M. S. R., & Kumari, K. W. S. N. (2020). Forecasting the red lentils commodity market price using SARIMA models. SN Business & Economics, 1(1), 20. https://doi.org/10.1007/s43546-020-00020-x

Hassani, H., Mashhad, L. M., Royer-Carenzi, M., Yeganegi, M. R., & Komendantova, N. (2025). White noise and Its misapplications: Impacts on time series model adequacy and forecasting. Forecasting, 7(1), 1-14. https://doi.org/10.3390/forecast7010008

Kumar, R., Lad, Y. A., & Kumari, P. (2025). Forecasting potato prices in Agra: comparison of linear time series statistical vs. Neural Network Models. European Potato Journal, 1-22. https://doi.org/10.1007/s11540-02409838-6

Mitra, D., & Paul, R. K. (2017). Hybrid time-series models for forecasting agricultural commodity prices. Model Assisted Statistics and Applications, 12(3), 255 264. https://doi.org/10.3233/MAS-170400

Paul, R. K., Yeasin, M., & Paul, A. K. (2022b). The volatility spillover of potato prices in different markets of India. Current Science (00113891), 123(3). https://doi.org/10.18520/cs/v123/i3/482-487

Paul, R., Panwar, S., Sarkar, S., Kumar, A., Singh, K., Farooqi, S., & Choudhary, V. (2013). Modelling and forecasting of meat exports from India. Agricultural Economics Research Review, 26(2), 249 - 255. https://doi.org/10.22004/ag.econ.162149

Paul, R., Yeasin, M., Kumar, P., Kumar, P., Balasubramanian, M., & Roy, H. et al. (2022a). Machine learning techniques for forecasting agricultural prices: A case of brinjal in Odisha, India. PLOS ONE, 17(7), e0270553. https://doi.org/10.1371/ journal. pone.0270553

Pepple, S. and Harrison, E. (2017). Comparative performance of Garch and Sarima techniques in the modeling of Nigerian board money. CARD International Journal of Social Science and Conflict Management, 2(4),258–270. https://casirmediapublishing.com/wp-content/uploads/2019/10/Pages-250-262.pdf

Rakshit, D., Paul, R. K., & Panwar, S. (2021). Asymmetric Price Volatility of Onion in India. Indian Journal of Agricultural Economics, 76(2), 245-260. https://doi.org/10.63040/25827510.2021.02.003

Rathod, S., Mishra, G. C., & Singh, K. N. (2017). Hybrid time series models for forecasting banana production in Karnataka State, India. Journal of the Indian Society of Agricultural Statistics, 71(3), 193-200. https://www.researchgate.net/publication/322165651_Hybrid_Time_Series_Models_for_Forecasting_Banana_Production_in_Karnataka_State_India

Şahinli, M. A. (2020). Potato price forecasting with Holt-Winters and ARIMA methods: A case study. American journal of potato research, 97(4), 336-346. https://doi.org/10/1007/s12230-020-09788-y

Sankaran, S. (2014). Demand forecasting of fresh vegetable product by seasonal ARIMA model. International Journal of Operational Research, 20(3), 315-330. https://doi.org/ 10.1504/ijor.2014.062453

Shankar, S. V., Chandel, A., Gupta, R. K., Sharma, S., Chand, H., Aravinthkumar, A., & Ananthakrishnan, S. (2024). Comparative study on key time series models for exploring the agricultural price volatility in Potato prices. Potato Research, 1-19. https://doi.org/10.21203/rs.3.rs-4011255/v1

Sharma, H., Kalamkar S.S., Raykundilya D.P., Makwana, M. (2020). Capturing the price volatility of onion and potato in selected markets of Gujarat. Annals of Arid Zone, 59(3&4), 95–00. https://dio.org/10.56093/aaz.v59i3%20&%204.109657

Singh, B. and Dutt, S. (2024). Seventy-five years of potato in India: An inspiring success story. Indian Farming, 74(5), 03–08. https://epubs.icar.org.in/index.php/IndFarm/article/view/151951

Singh, U., Praharaj, C. S., Singh, S. S., & Singh, N. P. (2016). Biofortification of food crops (1st ed). Springer India. https://doi.org/10.1007/978-81-322-2716-8

Sinha, M., Mukhopadhyay, P., & Sarkar, D. (2024). Interpreting potato price volatility through GARCH estimation: A novel evidence. In Growth, Poverty and Developmental Aspects of Agriculture, 93-106. https://doi.org/10.1108/978-1-83608-076-320241007