Vol. 7, Issue 10, Part B (2025)

Accurate forecasting of agricultural production is critical for food security, policy planning, and economic stability in India. This study employs the Auto-Regressive Integrated Moving Average (ARIMA) model to forecast food grain production using annual time series data from 1950 to 2024, sourced from the Reserve Bank of India (RBI). Following the Box-Jenkins methodology, the Augmented Dickey-Fuller (ADF) test confirmed stationarity (p < 0.05). Among competing models, ARIMA (0,1,1) was selected as optimal due to its lowest Akaike Information Criterion (AIC = 12.254) and statistically significant coefficients (p < 0.0001). Diagnostic checks, including the Ljung-Box Q test (p> 0.05) and Jarque-Bera test (p= 0.284), confirmed residual white noise and normality. The model forecasts a continued decline in food grain production, projecting values of 489.46, 451.64, and 413.81 (units) for 2025-2027, respectively. While the ARIMA model effectively captures short-term temporal patterns, its linear assumptions and exclusion of exogenous variables (e.g., climate, policy) limit long-term accuracy. This study underscores the utility of ARIMA as a policy tool for short-term planning while advocating for future integration of multivariate or machine learning models to enhance predictive robustness.