Abstract
Several factors, including weather vagaries, possess a serious threat to agricultural crop production in India and also are noteworthy risks to the economy. Crop yield depends on nutrition level of soils, fertilizer availability and cost, pest control, agro-meteorological input parameters like temperature, rainfall and other factors. Further, each particular crop needs specific growing weather conditions. Therefore, prognosticating crop yield is a challenging task for every nation. Statistical models are the most commonly used tools to forecast the crop yield, whereas statistical forecasting model for predicting dynamic behavior of crop yield should be able to take advantage not only of historical data of crop yield, but also the impact of various driving forces of the external environment. This paper describes both the linear regression and time-series models to predict crop yield efficiently and precisely. In particular, Bajra yield data for Alwar district of Rajasthan have been considered for empirical fitting of the models. Additionally, the selection of auxiliary variables, based on the knowledge of crop growth stages, has mediated the outperformance of time-series model.
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