Abstract Green gram (Vigna radiata), one of the major pulse crops cultivated in India, plays a significant role in nutritional security, agricultural sustainability, and rural livelihoods. However, fluctuations in market prices caused by seasonal production variability, climatic uncertainties, supply-chain disruptions, and policy interventions create substantial challenges for farmers and market participants. This study investigates the price dynamics and forecasting behaviour of green gram in India using advanced agricultural forecasting algorithms based on Box–Jenkins ARIMA modelling techniques. Monthly market price data from major agricultural markets during the period 2014–2025 were analyzed to identify trends, seasonality, volatility, and predictive patterns. The study employed stationarity testing, autocorrelation analysis, ARIMA/SARIMA model estimation, and forecast validation measures including RMSE, MAE, and MAPE. Results revealed significant seasonal price fluctuations and long-run upward trends in green gram prices. Among the competing models, the SARIMA (1,1,1)(1,1,1)12 model demonstrated superior forecasting performance with the lowest forecasting errors and high predictive accuracy. Residual diagnostic tests confirmed the adequacy and stability of the selected model. Forecast results indicate moderate price growth over the upcoming marketing seasons, suggesting continued demand pressure and market volatility. The insights highlight the effectiveness of agricultural algorithms in commodity price forecasting and provide valuable implications for policymakers, traders, farmers, and agribusiness stakeholders in planning production, procurement, storage, and marketing decisions. The study further recommends integrating machine learning and climate-sensitive forecasting models for enhanced agricultural market intelligence. Keywords: Green gram, ARIMA, SARIMA, agricultural algorithms, price forecasting, Box–Jenkins methodology, market volatility, India.
Meena et al. (Thu,) studied this question.