Follow
International Journal of Current Microbiology and Applied Sciences (IJCMAS)
IJCMAS is now DOI (CrossRef) registered Research Journal. The DOIs are assigned to all published IJCMAS Articles.
Index Copernicus ICI Journals Master List 2023 - IJCMAS--ICV 2023: 95.56 For more details click here
National Academy of Agricultural Sciences (NAAS) : NAAS Score: *5.38 (2020) [Effective from January 1, 2020] For more details click here

Login as a Reviewer


See Guidelines to Authors
Current Issues
Download Publication Certificate

Original Research Articles                      Volume : 13, Issue:1, January, 2024

PRINT ISSN : 2319-7692
Online ISSN : 2319-7706
Issues : 12 per year
Publisher : Excellent Publishers
Email : editorijcmas@gmail.com /
submit@ijcmas.com
Editor-in-chief: Dr.M.Prakash
Index Copernicus ICV 2018: 95.39
NAAS RATING 2020: 5.38

Int.J.Curr.Microbiol.App.Sci.2024.13(1): 53-60
DOI: https://doi.org/10.20546/ijcmas.2024.1301.006


Application of Stochastic Model in the Production of Sugarcane in India
R. Krishna Priya and Kausalya Nataraj*
Department of Statistics, PSG College of Arts and Science, Coimbatore, India
*Corresponding author
Abstract:

Forecasting is an essential tool to estimate the future trend of any crop shortly. There are various techniques in the present scenario for predicting future figures and Auto Regressive Integrated Moving Average (ARIMA) is one among them. Sugarcane is an imperative crop in India, keeping in view its importance for many areas of the country and its diverse uses. The present study was intended to check and identify the best forecasting model of sugarcane production in India using historical data between the years 2001 to 2020, based on the estimation of a suitable ARIMA model. The analysis of ACF & PACF of different series revealed that ARIMA was the most suitable model for forecasting based on diagnostics, such as ACF, PACF, and AIC. The selected ARIMA model predicted the sugarcane production for the immediate 10 years from 2021.


Keywords: Sugarcane Production, ACF, PACF, ARIMA, Box test


References:

Box, G. E. P., G. M. Jenkins. Time Series Analysis, Forecasting and Control. San Francisco, Holden-Day, California, USA, 1976.

Badmus, M., Ariyo, O., Badmus, M. A., Ariyo, O. S. Forecasting Cultivation Area and Production of Maize in Nigeria Using ARIMA Model. Asian J. Agric. Sci.(2011), 3 (3), 171–176.

Directorate of Economics and Statistics, Ministry of Agriculture and Farmers Welfare, Government of India.

Gopinath, M., Naveenapriyaa, M., Sindhu, T., Abinaya, K., &Prathiskaaarthi, S. (2021). Production of commericial crop prediction using arima model. NVEO-NATURAL VOLATILES & ESSENTIAL OILS Journal NVEO, 4985-4998.

Hossain, M. M., & Abdulla, F. (2016). Forecasting potato production in Bangladesh by ARIMA model. J Adv Stat1(4), 191-198.

Iqbal, N., Bakhsh, K., Maqbool, A., Abid Shohab, A. Use of the ARIMA Model for Forecasting Wheat Area and Production in Pakistan. J. Agric. Soc. Sci.(2005), 2 (1), 120–122.

Sankar, T. J., Prabakaran, R. Forecasting Milk Production in Tamilnadu. Int. Multidiscip. Res. J. (2012), 2 (1), 10–15.

ShaikNafeez Umar, T., Gangaram, O., HariBabu, G., Sathyanarayana Reddy, B., Ramana Murthy and Samreen Aalia, P. (2021). Application of Arima Models in Millet Production in Andhra Pradesh. Int.J.Curr.Microbiol.App.Sci. 10(7):525-531. https://doi.org/10.20546/ijcmas.2021.1007.057


Download this article as Download

How to cite this article:

Krishna Priya, S. R. and Kausalya Nataraj. 2024. Application of Stochastic Model in the Production of Sugarcane in India.Int.J.Curr.Microbiol.App.Sci. 13(1): 53-60. doi: https://doi.org/10.20546/ijcmas.2024.1301.006
Copyright: This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike license.

Citations