Forecasting Rice Productivity and Production of Odisha, India, Using Autoregressive Integrated Moving Average Models. (30th September 2014)

• Record Type: Journal Article
• Authors: Tripathi, Rahul; Nayak, A. K.; Raja, R.; Shahid, Mohammad; Kumar, Anjani; Mohanty, Sangita; Panda, B. B.; Lal, B.; Gautam, Priyanka
• Other Names: Tsadilas Christos Academic Editor.
• Is Part Of: Advances in agriculture. Volume 2014(2014)
• Publication Date: 2014-09-30
• Subjects: Agriculture -- Periodicals; Agriculture; Periodicals; 630
• DOI: 10.1155/2014/621313
• Languages: English
• ISSNs: 2356-654X

Abstract

Abstract : Forecasting of rice area, production, and productivity of Odisha was made from the historical data of 1950-51 to 2008-09 by using univariate autoregressive integrated moving average (ARIMA) models and was compared with the forecasted all Indian data. The autoregressive (p ) and moving average (q ) parameters were identified based on the significant spikes in the plots of partial autocorrelation function (PACF) and autocorrelation function (ACF) of the different time series. ARIMA (2, 1, 0) model was found suitable for all Indian rice productivity and production, whereas ARIMA (1, 1, 1) was best fitted for forecasting of rice productivity and production in Odisha. Prediction was made for the immediate next three years, that is, 2007-08, 2008-09, and 2009-10, using the best fitted ARIMA models based on minimum value of the selection criterion, that is, Akaike information criteria (AIC) and Schwarz-Bayesian information criteria (SBC). The performances of models were validated by comparing with percentage deviation from the actual values and mean absolute percent error (MAPE), which was found to be 0.61 and 2.99% for the area under rice in Odisha and India, respectively. Similarly for prediction of rice production and productivity in Odisha and India, the MAPE was found to be less than 6%.