Modeling and Monitoring Cereals Crop Production Based On Parametric and Nonparametric Regression Models

S.S. Suba¹ , A. Rajarathinam²

Section:Research Paper, Product Type: Journal-Paper
Vol.6 , Issue.4 , pp.16-29, Aug-2019

CrossRef-DOI:   https://doi.org/10.26438/ijsrmss/v6i4.1629

Online published on Aug 31, 2019

Copyright © S.S. Suba, A. Rajarathinam . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
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. 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. Nonparametric estimates of underlying growth functions are computed at each and every time points. Relative growth rates of crop productions are estimated based on the best fitted trend functions. In this study none of the parametric models are found suitable to study the trend. Nonparametric regression with independent error model is found suitable to study the trends. An increase in trend has been observed in the cereals crop production and productivity in spite of decrease in area under the crop. 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.

Key-Words / Index Term :
Adjusted R2, Shapiro –Wilk test, Run test, Kernal Smoothing, Nonparametric Regression

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