Rice provides 21% of global human per capita energy and 15% of per capita protein. Although rice protein ranks high in nutritional quality among cereals, protein content is modest. Rice also provides minerals, vitamins, and fiber, although all constituents except carbohydrates  are reduced by milling. The present study aims at the stochastic forecasting analysis for rice production like autoregressive (AR), moving average (MA) and autoregressive integrated moving average (ARIMA) processes to select the suitable model and to predict in  the future. This study is an attempt in the direction of finding out suitable methods for forecasting of rice production in India during the period 1950-2013. Based on ARIMA  and its components autocorrelation function (ACF), partial autocorrelation function (PACF), Bayesian information criterion (BIC) and Box-Ljung Q statistics and residuals estimated, ARIMA  is selected. Based on the chosen model, it could be predicted that the rice production would increase to 116.18 million tons in 2019 from 106.4 million tons in 2013 in India .

forecasting, rice production, ARIMA model, BIC.