Forecasting Rice Productivity and Production of Odisha, India, Using Autoregressive Integrated Moving Average Models
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) par...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Advances in Agriculture |
| Online Access: | http://dx.doi.org/10.1155/2014/621313 |
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| author | Rahul Tripathi A. K. Nayak R. Raja Mohammad Shahid Anjani Kumar Sangita Mohanty B. B. Panda B. Lal Priyanka Gautam |
| author_facet | Rahul Tripathi A. K. Nayak R. Raja Mohammad Shahid Anjani Kumar Sangita Mohanty B. B. Panda B. Lal Priyanka Gautam |
| author_sort | Rahul Tripathi |
| collection | DOAJ |
| description | 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%. |
| format | Article |
| id | doaj-art-095794ebbb3e4ba68d9859750a00fa9b |
| institution | Kabale University |
| issn | 2356-654X 2314-7539 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Agriculture |
| spelling | doaj-art-095794ebbb3e4ba68d9859750a00fa9b2025-02-03T05:58:13ZengWileyAdvances in Agriculture2356-654X2314-75392014-01-01201410.1155/2014/621313621313Forecasting Rice Productivity and Production of Odisha, India, Using Autoregressive Integrated Moving Average ModelsRahul Tripathi0A. K. Nayak1R. Raja2Mohammad Shahid3Anjani Kumar4Sangita Mohanty5B. B. Panda6B. Lal7Priyanka Gautam8Crop Production Division, Central Rice Research Institute, Cuttack, Odisha 753006, IndiaCrop Production Division, Central Rice Research Institute, Cuttack, Odisha 753006, IndiaCrop Production Division, Central Rice Research Institute, Cuttack, Odisha 753006, IndiaCrop Production Division, Central Rice Research Institute, Cuttack, Odisha 753006, IndiaCrop Production Division, Central Rice Research Institute, Cuttack, Odisha 753006, IndiaCrop Production Division, Central Rice Research Institute, Cuttack, Odisha 753006, IndiaCrop Production Division, Central Rice Research Institute, Cuttack, Odisha 753006, IndiaCrop Production Division, Central Rice Research Institute, Cuttack, Odisha 753006, IndiaCrop Production Division, Central Rice Research Institute, Cuttack, Odisha 753006, IndiaForecasting 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%.http://dx.doi.org/10.1155/2014/621313 |
| spellingShingle | Rahul Tripathi A. K. Nayak R. Raja Mohammad Shahid Anjani Kumar Sangita Mohanty B. B. Panda B. Lal Priyanka Gautam Forecasting Rice Productivity and Production of Odisha, India, Using Autoregressive Integrated Moving Average Models Advances in Agriculture |
| title | Forecasting Rice Productivity and Production of Odisha, India, Using Autoregressive Integrated Moving Average Models |
| title_full | Forecasting Rice Productivity and Production of Odisha, India, Using Autoregressive Integrated Moving Average Models |
| title_fullStr | Forecasting Rice Productivity and Production of Odisha, India, Using Autoregressive Integrated Moving Average Models |
| title_full_unstemmed | Forecasting Rice Productivity and Production of Odisha, India, Using Autoregressive Integrated Moving Average Models |
| title_short | Forecasting Rice Productivity and Production of Odisha, India, Using Autoregressive Integrated Moving Average Models |
| title_sort | forecasting rice productivity and production of odisha india using autoregressive integrated moving average models |
| url | http://dx.doi.org/10.1155/2014/621313 |
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