Application of Regression Models for Area, Production and Productivity Trends of Maize Zea mays Crop for Panchmahal Region of Gujarat State, India
The present investigation was carried out to study area, production and productivity trends and growth rates of maize Zea mays crop grown in Panchmahal region of Gujarat state, India for the period 1949 50 to 2007 08 based on parametric and nonparametric regression models. In parametric models different linear, non linear and time series models were employed. The statistically most suited parametric models were selected on the basis of adjusted R2, significant regression co efficient and co efficient of determination R2 . Appropriate time series models were fitted after judging the data for stationarity. The statistically appropriate model was selected on the basis of various goodness of fit criteria viz. Akaike’s Information Criterion, Bayesian Information Criterion, RMSE, MAE , assumptions of normality andindependence of residuals. In nonparametric regression optimum bandwidth was computed by cross validation method. ‘Epanechnikov kernel’ was used as the weight function. Nonparametric estimates of underlying growth function were computed at each and every time point. Residual analysis was carried out to test the randomness. Relative growth rates of area, production and productivity were estimated based on the best fitted trend function. Linear model was found suitable to fit the trends in area and production of maize crop whereas for the productivity nonparametric regression without jump point emerged as the best fitted trend function. The compound growth rate values obtained for the years 1949 50 to 2007 08 in area, production and productivity of the maize crop showed that the production had increased at a rate of 0.49 per cent per annum due to combined effect of increase in area and productivity at a rate of 0.30 and 0.21 per cent per annum respectively.
Adjusted R2, stationarity, akaike’s information criterion, bayesian information criterion, lijung and box test, cross validation, band width
R.S. Parmar | S.H. Bhojani | G.B.Chaudhari