Agricultural Science Digest
SCOPUS
  • Year: 2025
  • Volume: 45
  • Issue: 5

Forecasting of Arecanut in India using Time Series Model

  • Author:
  • Pradeep Mishra1,*, Arti2, Bitan Mondal2, Rajnee Sharma3, Binita Kumari4, Tufleuddin Biswas5, Soumik Ray5
  • Total Page Count: 7
  • Page Number: 907 to 913

1Department of Agricultural Statistics, College of Agriculture, Rewa, Jawaharlal Nehru Agriculture University, Jabalpur-486 001, Madhya Pradesh, India

2Department of Agricultural Economics, Visva-Bharati, Santiniketan, Bolpur-731 236, West Bengal, India

3Department of Agricultural Economics, Rashtriya Kisan PG College, Shamli-247 776, Meerut, Uttar Pradesh, India

4Department of Horticulture, College of Agriculture, Jawaharlal Nehru Agriculture University, Jabalpur-482 004, Madhya Pradesh, India

5Centurion University of Technology and Management, Paralakhemundi-761 211, Odisha, India

*Corresponding Author: Pradeep Mishra, Department of Agricultural Statistics, College of Agriculture, Rewa, Jawaharlal Nehru Agriculture University, Jabalpur-486 001, Madhya Pradesh, India, Email: pradeepjnkvv@gmail.com

Online published on 29 October, 2025.

Abstract

Arecanut is popularly known as supari and is grown in many parts of the country. India maintained its first place in production among all the countries. In total world's area and production, India contributes about 49 per cent and 59 per cent respectively. The area has expanded to various states such as Tamil Nadu, West Bengal, Maharashtra, Andhra Pradesh, Goa, Meghalaya and Tripura etc.

The data from 1960-61 to 2015-16 is used to build the model, whereas data from 2016-17 to 2019-20 is used to validate the model. Appropriate statistical steps were adopted for model building and model validation. Holt's linear and Holt's exponential and ARIMA models is used in the study to forecast area, production and productivity for next five years from 2021 to 2025.

The results from the study revealed that Holt's winter Exponential was the best model for predicating area and production whereas ARIMA (0, 1, 1) model was found best suited for predicating productivity.

Keywords

Area, Arecanut, ARIMA, Forecasting, Modelling, Prediction, Productivity