Econometric Modeling and Forecasting of Arabica and Robusta Coffee Production for Sustainable Agriculture Development

Abstract

In the present study, we have used Box-Jenkins approaches an Autoregressive Integrated Moving Average model (ARIMA) for modeling and forecasting of annual amount of Arabica and Robusta coffee production and yield (ARCPY) in India. In this study used time series data was collected from the official website of the coffee board of India from 1986 to 2023 (38 observations). Augmented Dickey-Fuller (ADF) test has used for testing the stationarity of the time series, and the appropriate ARIMA model has selected based on minimum Akaike Information Criterion (AIC). The ARIMA models has compared with the other ARIMA models with respect to forecast accuracy measures, and the residuals has diagnosed for possible presence of autocorrelation, and white noise heteroscedasticity (WNH) test of the fitted models. The MAPE value of ACP and ACY has 8.94 and 9.35 percent respectively shows highly accurate forecasting percentage rate respectively. While, the MAPE value of RCP and RCY has 16.03 and 11.43 percent respectively shows good accurate forecasting percentage rate respectively. Thus, we found the ARIMA (2, 1, 4), (3, 1, 2), (0, 1, 3) and (2, 0, 1) models for Arabica and Robusta coffee; which has observed as the best suitable model for predicting the future annual amount of Arabica coffee production (ACP), Arabica coffee yield (ACY), Robusta coffee production (RCP) and Robusta coffee yield (RCY) respectively, and we have estimated that the annual amount of ACP and ACY achieved in the year 2023-24 from 97379.67 MTs, and 472.29 kg/hectare respectively to 93272.91 MTs, and 379.31 kg/hectare respectively in the year 2034-35 will decrease, and the annual amount of RCP and RCY achieved in the year 2023-24 from 268655.21 MTs, and 1110.68 kg/hectare respectively to 318614.85 MTs, and 1012.90 kg/hectare respectively in the year 2034-35 will reach.