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Abstract
Forecast of productivity (yield) has an importance over production and area separately because it depends on both. Trend of the same reveals the necessity of the resources to be managed, for increasing yield in future. The forecast values of the series are obtained using autoregressive integrated moving average (ARIMA) model and the trend is determined by the means of Mann Kendall’s trend test. In the present work we have found that the productivity of rice for overall country shows an increasing trend. Mann Kendal’s trend analysis reported that the productivity has a steadily increasing trend which was also evident from the Sen’s slope coefficient (Q). ARIMA (1,1,1) model with constant was found to be appropriate model for forecasting the productivity of rice. The forecast values were obtained for the subsequent four years starting from 2018 to 2021. Forecast error was also calculated and it was found to be less than 2 per cent i.e., 1.36 per cent.
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References
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- Kolliesuah, N.P., Saysay, J.L., Zinnah, M.M., Freeman, A.T. & Chinenye, D. (2020). Trend analysis of production, consumption and export of cashew crop in West Africa. African Crop Science Journal, 28(s1), 187-202. DOI: https://doi.org/10.4314/acsj.v28i1.14S
- Manoharan, M.& Ramalakshmi, T. (2015). Cost, returns and resource use efficiency in garlic cultivation in dindigul district. International Journal of Business and Administration Research Review, 3(10), 16-25.
- Nath, B., Dhakre, D. S. & Bhattacharya, D. (2019). Forecasting wheat production in India: An ARIMA modelling approach. Journal of Pharmacognosy and Phytochemistry, 8(1), 2158-65.
- Nath, B., Dhakre, D. S. & Bhattacharya, D. (2020). The challenge of selecting the best forecasting model for a time series data. Journal of Food, Agriculture and Environment, 18(2), 97-102.
- Tripathi, R., Nayak, A.K., Raja, R., Shahid, M., Kumar, A., Mohanty, S., Panda, B.B., Lal, B. & Gautam, P. (2014). Forecasting Rice Productivity and Production of Odisha, India, Using Autoregressive Integrated Moving Average Models. Advances in Agriculture, (1), 1-9. DOI: https://doi.org/10.1155/2014/621313
References
Contreras, J., Espinola, R.F., Nogales, J. & Conejo, A.J. (2003). ARIMA models to predict next-day electricity prices. IEEE Transactions on Power Systems, 18(3), 1014-20. DOI: https://doi.org/10.1109/TPWRS.2002.804943
Ghimire, D., Lamsal, G., Paudel, B., Khatri, S. & Bhusal, B. (2018). Analysis of trend in area, production and yield of major vegetables of Nepal. Trends in Horticulture, 1(2), 1-11. DOI: https://doi.org/10.24294/th.v1i2.914
Kolliesuah, N.P., Saysay, J.L., Zinnah, M.M., Freeman, A.T. & Chinenye, D. (2020). Trend analysis of production, consumption and export of cashew crop in West Africa. African Crop Science Journal, 28(s1), 187-202. DOI: https://doi.org/10.4314/acsj.v28i1.14S
Manoharan, M.& Ramalakshmi, T. (2015). Cost, returns and resource use efficiency in garlic cultivation in dindigul district. International Journal of Business and Administration Research Review, 3(10), 16-25.
Nath, B., Dhakre, D. S. & Bhattacharya, D. (2019). Forecasting wheat production in India: An ARIMA modelling approach. Journal of Pharmacognosy and Phytochemistry, 8(1), 2158-65.
Nath, B., Dhakre, D. S. & Bhattacharya, D. (2020). The challenge of selecting the best forecasting model for a time series data. Journal of Food, Agriculture and Environment, 18(2), 97-102.
Tripathi, R., Nayak, A.K., Raja, R., Shahid, M., Kumar, A., Mohanty, S., Panda, B.B., Lal, B. & Gautam, P. (2014). Forecasting Rice Productivity and Production of Odisha, India, Using Autoregressive Integrated Moving Average Models. Advances in Agriculture, (1), 1-9. DOI: https://doi.org/10.1155/2014/621313