Advance estimates of significant cereal and commercial crops are given by the Directorate of Economics and Statistics and the central Ministry of Agriculture, Cooperation & Farmers’ Welfare. However, the final estimates are released a few months after the actual harvest of the crops. In this study, ARIMA and State-Space models have been developed for sugarcane yield forecasting in Ambala and Karnal districts of Haryana. The above-mentioned models have been developed using yield data of sugarcane crop for the time period 1966-67 to 2009-10 of Ambala and Karnal districts. The validity of fitted models has been tested over the years 2010-11 to 2016-17. The forecasting performance of the developed models has been studied using percent deviations of sugarcane yield forecasts in relation to the actual yield, and root means squared errors. It has been observed that state-space models outperform the popular ARIMA models for forecasting of sugarcane yield in Northern Agro-climatic Zone of Haryana.
ACF, ARIMA, PACF, State-Space models, Stationarity, Yield forecasting
advanc-es and case studies in system identification (R. Mehra and D.G. Lainiotis (Eds.)). Academic Press, New York.
Aoki, M. (1987). State Space modeling of time series. Springer, Berlin.
Box, G.E.P. and Jenkins, G.M. (1976). Time series analysis: Forecasting and control. Holden Day, San Franscisco.
Commandeur, J.J.F. and Koopman, S. (2007) An introduction to state space time series analysis, Oxford University Press, Oxford, USA.
Durbin, J. and Koopman, S. J. (2002) A simple and efficient simulation smoother for state space time series analysis, Biometrika,89, 603-616.
Harvey, A.C. (1989) Forecasting, Structural Time Series and the Kalman Filter, Cambridge University Press, Cambridge, UK.
Hooda E.and Verma U. (2019). Unobserved components model for forecasting sugarcane yield in Haryana. Journal of Applied and Natural Science. 11(3): 661-665. doi.org/10.31018/jans.v11i3.2144.
Kitagawa, G. and Gersch, W. (1984). A smoothness priors-state space modeling of time series with trend and seasonality. J. Amer. Statist. Assoc. 79: 378-389.
Ljung, G.M. and Box, G.E.P. (1978). On a measure of lack of fit in time series models. Biometrika, 6: 297-303.
Marquardt, D.W. (1963). An algorithm for least-squares estimation of non-linear parameters. J. Soc. Ind. Appl. Math. 2 : 431-441.
Mwanga, D., Ongala, J. and Orwa, G. (2017) Modeling sugarcane yields in the Kenya sugar industry: A SARIMA model forecasting approach, International Journal of Statistics and Applications, ISSN: 2168-5193 e-ISSN: 2168-5215, 7(6), 280-288.
Pankratz, A. (1983) Forecasting with univariate Box-Jenkins models: concepts and cases, John Wiley & Sons, New York, USA.
Piepho, H.P. and Ogutu, J.O. (2007). Simple state-space models in a mixed model framework. Amer. Statist. 61 : 224 -232.
Ravichandran, S. and Prajneshu (2001). State space modelling versus ARIMA time series modelling, Journal of the Indian Society of Agricultural Statistics, 54(1), 43-51.
Suman and Verma U. (2018). Linear mixed models for sugarcane yield estimation in Haryana (India). Int. J. Agricullt. Stat. Sci., 14(1), 43-47.
This work is licensed under Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) © Author (s)