Research on World Agricultural Economy

Comparative Analysis of Price Forecasting Models for Garlic (Allium sativum L.) in Kota District of Rajasthan, India

Surjeet Singh Dhaka

Department of Applied Agriculture, Central University of Punjab, Bathinda, Punjab, 151401, India

Urmila

Department of Applied Agriculture, Central University of Punjab, Bathinda, Punjab, 151401, India

Dharavath Poolsingh

Department of Applied Agriculture, Central University of Punjab, Bathinda, Punjab, 151401, India

DOI: https://doi.org/10.36956/rwae.v4i4.915

Received: 30 July 2023; Received in revised form: 10 September 2023; Accepted: 25 September 2023; Published: 16 October 2023

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Abstract

Garlic is a well-known spice in India, and Rajasthan is the country's second-largest producer of garlic after Madhya Pradesh. Accurate price predictions are crucial for agricultural commodities, as they significantly impact the accessibility of food for consumers and the livelihoods of farmers, governments, and agribusiness industries. Governments also use these forecasts to support the agricultural sector and ensure food security. A study was conducted in Rajasthan's Kota district to analyze the wholesale price of garlic using data from July 2021 to July 2023 from the Kota fruit and vegetable market. The study used simple moving average (SMA), simple exponential smoothing (SES), and autoregressive integrated moving average (ARIMA) models to forecast garlic prices. The models were validated through mean absolute deviation (MAD), mean squared error (MSE), mean absolute percentage error (MAPE), root mean squared error (RMSE), correlation coefficient (r), and coefficient of variation (CV). The research was conducted utilizing Microsoft Excel and R Studio version 4.2.2 for Windows, and the results showed that the ARIMA (1,0,0) with a non-zero mean model had a strong correlation coefficient (r = 0.91**) and accurately predicted the variation in garlic prices. Based on the analysis, it is recommended to use this model for forecasting and making informed decisions.

Keywords: Agricultural commodities; ARIMA model; Garlic; Informed decisions; Market intelligence; Price forecasting models

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