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Plant Science Today (PST)

Research Articles

Vol. 12 No. sp1 (2025): Recent Advances in Agriculture by Young Minds - II

An empirical analysis of black gram (Vigna mungo L.) price forecasting using auto regressive integrated moving average (ARIMA) in selected Indian markets

DOI
https://doi.org/10.14719/pst.9990
Submitted
11 June 2025
Published
05-08-2025 — Updated on 23-08-2025
Versions

Abstract

This study analyzes the price dynamics and forecasting patterns of black gram (Vigna mungo L.) in India, focusing on the markets of Villupuram (Tamil Nadu) and Rajgarh (Madhya Pradesh) over 20-year period (2004-2024). Compound annual growth rate (CAGR), seasonal indices, standard deviation and coefficient of variation and auto regressive integrated moving average (ARIMA) models were used for time series analysis and forecasting future prices. This research examines long-term trends, seasonal patterns, forecasting accuracy and price volatility. Results show that Tamil Nadu demonstrated superior performance in black gram cultivation compared to the national averages across all parameters. The analysis of the seasonal indices reveals distinct pricing patterns between the two markets, with the Villupuram market exhibiting higher price volatility and clear seasonal peaks during the period of post-harvest periods, whereas the Rajgarh market maintains stable pricing throughout the year. The assessment of price stability highlights differing volatility characteristics between the markets, with varying absolute and relative price fluctuations. ARIMA forecasting models demonstrate a satisfactory level of accuracy for both markets, providing a reliable tool for price prediction. These findings offer a valuable insight for farmers, traders and policymakers to make informed decisions regarding the production planning, strategies of storage and market interventions, thereby promoting sustainable black gram cultivation and enhancing market efficiency.

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