Price forecasting of chickpea using the ARIMA model for major markets in Maharashtra
DOI:
https://doi.org/10.53550/jfl.v39i1.2497Keywords:
ARIMA, Chickpea, Forecasting, Maharashtra, Price, Seasonal indicesAbstract
The present study examined price behavior and arrivals of chickpea in five major APMCs of Maharashtra viz., Akola, Washim, Khamgaon, Murtizapur, and Akot, using 18 years (2005–06 to 2022–23) of monthly data. Seasonal indices were estimated through the moving average method, and price forecasting was carried out using the ARIMA model. The results revealed distinct seasonal patterns, with peak prices generally observed during postharvest months, while lower prices prevailed in the early months of the year. Forecast accuracy remained high, with deviations mostly within ±10%, except during unexpected market shocks. The study highlights the usefulness of price forecasting models in strengthening market intelligence and supporting farmers’ decisions on storage and timing of sale to enhance price realization.
References
Acharya SP, Basavaraja H, Kunnal LB, Mahajanashetti SB and Bhat AR. 2011. Crop diversification in Karnataka: An economic analysis. Agricultural Economics Research Review 24(4): 351–358.
Anonymous 2021. Agricultural Statistics at a Glance. Ministry of Agriculture and Farmers Welfare, Government of India.
Barghouti S, Kane S, Sorby K and Ali M. 2004. Agricultural Diversification for the Poor: Guidelines for Practitioners. Agriculture and Rural Development Discussion Paper 1, The World Bank.
Box GEP and Jenkins GM. 1970. Time Series Analysis: Forecasting and Control. Holden-Day, San Francisco.
Chand R and Raju SS. 2008. Instability in Indian agriculture during different phases of technology
and policy. Indian Journal of Agricultural Economics 63(2): 283–288.
Expert Market Research. 2025. Chickpeas Market Size, Share and Growth Analysis Report: Forecast Trends and Outlook (2025–2034). Industry Report, Expert Market Research.
FAO. 2019. Pulses: Nutritious Seeds for a Sustainable Future. Food and Agriculture Organization of the United Nations, Rome.
Gaikwad PD, More SS, Perke DS, Chavan RV and Thombre RF. 2025. Price dynamics of chickpea in India: An econometric analysis. International Journal of Agriculture and Food Science 7(9): 221–227. https://doi.org/10.33545/2664844X.2025.v7.i9c.748
Gaur PM, Tripathi S, Gowda CLL and Rao BVS. 2014. Chickpea Production and Consumption Patterns in the Semi-Arid Tropics. ICRISAT, Patancheru, India.
Government of India 2025. Third Advance Estimates of Crop Production 2024–25. Ministry of Agriculture and Farmers’ Welfare, Government of India, New Delhi. Pp. 95–97. Available from: https://desagri.gov.in
Graham PH and Vance CP. 2003. Legumes: Importance and constraints to greater use. Plant Physiology 131(3): 872–877. https://doi.org/10.1104/pp.017004
Gujarati DN and Porter DC 2009. Basic Econometrics (5th Ed.). McGraw-Hill, New York. 922 pp.
Gupta AK, Rao VS and Singh AK. 2018. Forecasting of arrivals and prices of chickpea in Chhattisgarh. International Journal of Agricultural and Statistical Sciences 14(1): 421–426.
Jadhav V, Swamy MN and Gracy CP. 2018. Supply– demand gap analysis and projection for major pulses in India. Economic Affairs 63(1): 277–285.
Joshi PK and Saxena R. 2015. A profile of pulses production in India: Facts, trends and opportunities. Indian Journal of Agricultural Economics 70(1): 1–21.
Kamble R, Lad D, Rajguru A, Shinde P, Bhakre M and Patil O 2025. Assessment of G × E interaction for yield and yield-attributing traits in chickpea (Cicer arietinum L.) genotypes. International Journal of Advanced Biochemistry Research 9(8S): 1847–1851.
Kavitha N, Manikandan R and Balaji C 2020. Forecasting agricultural commodity prices using time series analysis. Indian Journal of Agricultural Economics 75(3): 335–341.
Kendall MG and Stuart A 1976. The Advanced Theory of Statistics (Vol. 3). Charles Griffin, London. 552 pp.
Khairnar KY, Dorge JT, Amale AJ and Bhagat AA. 2026. Chickpea price dynamics in Maharashtra: Evidence from seasonal indices, cointegration and ARIMA forecasting. Journal of Agricultural Sciences – Sri Lanka 21(1): 187–199. https://doi.org/10.4038/jas.
v21i1.11845
Kumar A, Kumar V, Chetna, Ghalawat S, Kaur J, Kumari K, Saharan H, Chhabra S and Rai B. 2024. Forecasting cotton (Gossypium spp.) prices in major Haryana markets: A time series and ARIMA approach. Indian Journal of Agricultural Sciences 94(9): 1013–1018.
Murugananthi D, Shivakumar KM, Palanichamy VN, Prabha AS, Somasundaram E, Rohini A, Devi PR, Selvanayaki S and Kavitha PG. 2024. Demand and supply projections for pulses in India. Legume Research 47(8): 1335–1341. https://doi.org/10.18805/LR-5346
Naidu AV and Kala RS. 2016. Price forecasting of agricultural commodities using ARIMA model. Agricultural Economics Research Review 29(2): 261– 267.
Noonari S, Irfana NM, Raiz AB, Muhammad IK and Ali S. 2015. Price flexibility and seasonal variations of major vegetables in Sindh, Pakistan. Journal of Food Processing and Technology 6(12).
Sherly A, Christo MS and Elizabeth JV. 2025. A hybrid approach to time series forecasting: Integrating ARIMA and Prophet for improved accuracy. Results in Engineering 27. https://doi.org/10.1016/j.rineng.2025.105703
Singh R, Meena ML and Yadav R. 2017. Market integration and price forecasting of pulses in India. Economic Affairs 62(4): 593–600.
Tiwari AK and Shivhare AK. 2018. Pulses in India: Retrospect and Prospects. Directorate of Pulses Development, Government of India, Bhopal. Pp. 1–346.
USDA 2022. World Agricultural Supply and Demand Estimates: Pulses Outlook. United States Department of Agriculture, Foreign Agricultural Service, Washington DC.
