MODELLING AND FORECASTING IN DAIRY MILK PRODUCTION  OF  INDIA USING TIME SERIES APPROACHES

H.G. Koladiya; D.M. Mane; A.R. Ahlawat; M.V. Darji

doi:10.56093/ijvasr.v54i5.172408

Authors

H.G. Koladiya Ph.D Scholar, Department of Animal Genetics and Breeding, College of Veterinary Science and Animal Husbandry, Kamdhenu University, Anand, Gujarat - 388 001
D.M. Mane Post-Graduate Scholar, Department of Animal Genetics and Breeding, College of Veterinary Science and Animal Husbandry, Kamdhenu University, Anand, Gujarat - 388 001
A.R. Ahlawat Professor and Head, Department of Animal Genetics and Breeding, College of Veterinary Science and Animal Husbandry, Kamdhenu University, Anand, Gujarat - 388 001
M.V. Darji Research Associate, Department of Veterinary Biotechnology, College of Veterinary Science and Animal Husbandry, Kamdhenu University, Anand, Gujarat - 388 001

https://doi.org/10.56093/ijvasr.v54i5.172408

Keywords:

ARIMA, MA, VAR, Forecasting, Milk production

Abstract

This study investigates the application of time series models for forecasting milk production in India. Using secondary data from 2001–02 to 2022–23, we evaluated four forecasting techniques, Exponential Smoothing (ES), Moving Average (MA), Vector Auto Regression (VAR), and Auto Regressive Integrated Moving Average (ARIMA). The ARIMA (1,1,0) model emerged as the best fit for short-term forecasting, while MA demonstrated better performance for long-term projections. The VAR model achieved an adjusted R² of 1.00, indicating a near-perfect fit, although its predictive capacity needs careful interpretation. Forecasting using ARIMA projected India’s milk production to reach approximately 394 million tonnes by 2032–33, with a Compound Annual Growth Rate (CAGR) of 5.29%. These findings hold significant relevance for policymakers aiming to devise data-driven strategies to support sustainable dairy sector growth.

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