Coffee Production Modelling in India Using Nonlinear Statistical Growth Models

Efforts have been made in this paper, to develop appropriate nonlinear statistical models with a view to provide analytical approach to describe the coffee production trends in India. To this end, attempts were made to apply six nonlinear statistical growth models. The parameters of each model were estimated using Levenberg-Marquardt (LM) iterative method. The main assumptions of 'independence' and 'normality' of error terms were examined by using respectively, the 'Run-test' and 'Shapiro-Wilk test'. The best model was selected based on the performance of several model goodness of fit criteria viz., R², MAE, MSE, RMSE, MAPE, AIC and BIC. MMF and Logistic models were found to be quite successful for describing the pattern of coffee production. Forecast values were also computed using two best fitted models. A comparative study indicated that both selected models were performed similarly for forecasting coffee production for the years 2015 and 2020.