Development of State Space Models with Weather as Exogenous Input for Sugarcane Yield Prediction in
Parameter constancy is a fundamental issue for empirical models to be useful for forecasting, analyzing or testing any theory. Unlike classical regression analysis, the state space models (SSM) are time varying parameters models as they allow for known changes in the structure of the system over time and provide a flexible class of dynamic and structural time series models. The work deals with the development of state space models with weather as exogenous input for sugarcane yield prediction in Ambala and Karnal districts of Haryana. The state space models with weather as exogenous input using different types of growth trends viz., polynomial splines; PS(1), PS(2) and PS(3) have been developed however PS(2) with weather input was selected as the best suited model for this empirical study. Timely and effective pre-harvest forecast of crop yield helps in advance planning, formulation and implementation of policies related to the crop procurement, price structure, distribution and import-export decisions etc. These forecasts are also useful to farmers to decide in advance their future prospects and course of action. The sugarcane yield forecasts based on state space models with weather input showed good agreement with state Department of Agriculture and Farmers’ Welfare yield(s) by showing nearly 4 percent average absolute relative deviations in the two districts.
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