2024, Vol. 9, Special Issue 3
Forecasting sugarcane productivity of India using ARIMA models
Author(s): Ajay Kumar, Satish Manda, Raj Kumar, Rajender Kumar and Nitin Tanwar
Abstract: India has a well-established system of collecting agricultural statistics. Sugarcane is one of the important commercial crops in India. Crop productivity forecasts and crop production estimates are necessary for national food security including early determination of the import/export plan and price and to provide timely information for optimum management of growing crops. This paper attempts forecasting the sugarcane productivity of India using the Univariate Auto Regressive Integrated Moving Average (ARIMA) models and the time series data taken from 1980-81 to 2022-23.The results of the study revealed that the ARIMA model (3, 2, 0) have been selected as best models among all the models for prediction of the sugarcane productivity on the basis of Schwarz's Bayesian Information Criterion, Mean absolute prediction error (MAPE), MAE and R2 for sugarcane productivity of India. The performances of models are validated by comparing with actual values.
Pages: 108-114 | Views: 95 | Downloads: 4Download Full Article: Click HereHow to cite this article:
Ajay Kumar, Satish Manda, Raj Kumar, Rajender Kumar, Nitin Tanwar. Forecasting sugarcane productivity of India using ARIMA models. Int J Stat Appl Math 2024;9(3S):108-114.