Price Behaviour and Price Forecasting of Tomato in Koyambedu Market, Tamil Nadu

Lekha Priya  K; Preethika Suresh; Nikhitha Yeturu; Neha Bhargavi; Merlin Mathew; Paul Mansingh J; Nisha A.

doi:10.61707/xperm644

Authors

Lekha Priya K Mansingh J.3, Nisha A.3, School of Agricultural Innovations and Advanced Learning (VAIAL), Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, India
Preethika Suresh Mansingh J.3, Nisha A.3, School of Agricultural Innovations and Advanced Learning (VAIAL), Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, India
Nikhitha Yeturu Mansingh J.3, Nisha A.3, School of Agricultural Innovations and Advanced Learning (VAIAL), Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, India
Neha Bhargavi Mansingh J.3, Nisha A.3, School of Agricultural Innovations and Advanced Learning (VAIAL), Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, India
Merlin Mathew Department of Agricultural Extension and Economics, VAIAL, VIT, Vellore, Tamil Nadu, India.
Paul Mansingh J Department of Agricultural Extension and Economics, VAIAL, VIT, Vellore, Tamil Nadu, India.
Nisha A. Department of Agricultural Extension and Economics, VAIAL, VIT, Vellore, Tamil Nadu, India.

DOI:

https://doi.org/10.61707/xperm644

Keywords:

Price analysis, Market prices, Price forecasting, ARIMA

Abstract

This study aimed to comprehend the price behavior of tomato crops and forecast their prices in the Koyambedu market in Tamil Nadu, one of the major vegetable markets in South India. Secondary data on price of tomatoes were collected from Agricultural Produce Market Committees (APMCs) for a period of 8 years, from 2014 to 2022. Statistical tools such as trend analysis and seasonality indices were computed to understand the movement of prices in the Koyambedu market. Results revealed peak market prices in November and lowest in March, with a positive trendline indicating a gradual price increase over the study years. Comparative analysis between Autoregressive Integrated Moving Average (ARIMA) and Seasonal-ARIMA (SARIMA) assessed price prediction accuracy. The periodogram highlighted significant seasonality, guiding the use of SARIMA to forecast 2023 monthly prices. Predicted prices ranged from 3675.00 Rs/Qtl in November to 1178.76 Rs/Qtl in May.