Suman Suman Urmil Verma


Box and Jenkins’ Autoregressive Integrated Moving Average (ARIMA) models are widely used for analyzing and forecasting the time-series data. In this approach, the underlying parameters are assumed to be constant however the data in agriculture are generally collected over time and thus have the time-dependency in parameters. Such data can be analyzed using state space (SS) procedures by the application of Kalman filtering technique. The purpose of this article is to illustrate the usefulness of state space models in sugarcane yield forecasting and to pro-vide some empirical evidence for its superiority over the classical time-series analysis. ARIMA and state space models individually could provide the suitable relationship(s) to reliably forecast the sugarcane yield in Karnal, Ambala, Kurukshetra, Yamunanagar and Panipat districts of Haryana (India). However, the state space models with lower error metrics showed the superiority over ARIMA models for this empirical study. The sugarcane yield forecasts based on SS models in the districts under consideration showed good agreement with State Department of Agriculture (DOA) yields by showing 3-6 percent average absolute deviations.


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Autocorrelation function, Kalman filtering technique, State space procedures, Akaike’s information criterion, Sugarcane yield forecast

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Suman, S., & Verma, U. (2017). State space modelling and forecasting of sugarcane yield in Haryana, India. Journal of Applied and Natural Science, 9(4), 2036-2042. https://doi.org/10.31018/jans.v9i4.1485
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