A Study on Cereals Crops Production Based on Parametric and Nonparametric Regression Models

This chapter investigated about Cereals Crop Production Based on Parametric and Nonparametric Regression Models. Nonparametric regression and semi-parametric regression technique for functional estimation has become increasingly popular as a tool for data analysis. These techniques impose only few assumptions about shape of function and therefore it is more flexible than usual parametric regression approaches. The present investigation is carried out to study the trends in cereals crops production in India for the period 1960- 1961 to 2016-2017 based on the parametric and nonparametric regression models. In parametric models different linear models are employed. Nonparametric estimates of underlying growth functions are computed at each and every time points. Residual analysis is carried out to test the randomness as well as normality. A relative growth rate is calculated based on best fitted models. The statistically most suited parametric models are selected on the basis of highest adjusted R2, significant regression co-efficient and co-efficient of determination (R2). Appropriate model is selected based on the model performance measures such as, Root Mean Square Error, Mean Absolute Error, Mean Absolute Percentage Error, assumptions of normality and independence of residuals. At each and every time point, nonparametric estimates of the underlying growth functions are calculated. Based on the best fitting trend functions, crop output relative growth rates are estimated. None of the parametric models in this study were determined to be useful for studying the trend. The analysis of trends is determined to be best done using nonparametric regression with independent error model. Despite a reduction in the amount of land planted with the crop, an increase in trend has been seen in cereal crop productivity and production. The average percent growth rate values obtained for the successive years during the study period for the area, production and productivity when averaged shows that the production has increased at a rate of 1.03 per cent per annum due improvement in yield (2.09 per cent per annum) even though the area decreased at a rate of 1.08 per cent per annum. It is concluded that nonparametric regression with independent error model is selected as the best fitted trend function for the area, production and productivity.