Forecasting Milk Production in India: Strategic Insights for Policymakers and Farmers
DOI:
https://doi.org/10.48165/IJEE.2025.61203Keywords:
Indian dairy industry, ARIMA, Holt’s exponential smoothing, Sustainable developmentAbstract
India’s dairy sector plays a critical role in rural income, employment, and food security, contributing significantly to the nation’s GDP. This study forecasts India’s milk production for the period 2024–2033, using historical data from 1981–2023 obtained from the Department of Animal Husbandry and Dairying, Government of India. The study adopts ARIMA and Holt’s Exponential Smoothing models for the forecasting of milk production in India, both the models reflected strong fits, with ARIMA excelling in capturing temporal structures and Holt’s model focusing on linear trends. Performance metrics highlighted the high accuracy of both models, with R-squared values exceeding 0.99 and minimal error margins. The research provides actionable insights for farmers, policymakers, and other stakeholders. The results project milk production to increase steadily, reaching 315.6–321.4 million tons by 2033. The study highlights the potential of leveraging these forecasts for strategic planning, including synchronizing production with demand, improving market stability, and addressing infrastructural and environmental challenges.Downloads
References
Box, G. (2013). Box and Jenkins: time series analysis, forecasting and control. In A Very British Affair: Six Britons and the Development of Time Series Analysis During the 20th Century (pp. 161–215). Palgrave Macmillan UK.
Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons. Chand, R. (2023). India’s White Revolution Achievements and the Next Phase National Institution for Transforming India Government of India New Delhi. https://www.niti.gov.in/sites/default/files/
2023-04/Working-Paper-Indias-White-Revolution.pdf Chatfield, C. (2016). The Analysis of Time Series: An Introduction (6th ed.). Chapman and Hall/CRC.
Department of Animal Husbandry & Dairying, G. of I. (2023). Annual Report 2022-23. https://dahd.gov.in/sites/default/files/2023-06/ FINALREPORT2023ENGLISH.pdf
Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366), 427–431.
Fos, A. (2023, March 8). Celebrating women in dairy through engaging case studies. International Dairy Federation. https:// fil-idf.org/news_insights/women-and-dairy/#_ftn3
Holt, C. C. (2004). Forecasting trends and seasonals by exponentially weighted averages. International Journal of Forecasting, 20(1), 5–10.
Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and Practice. OTexts. https://doi.org/10.1007/978-3-319-52452- 8
Hyndman, R. J., & Khandakar, Y. (2008). Automatic time series forecasting: The forecast package for R. Journal of Statistical Software, 27(3), 1–22.
Kaur, N., & Toor, J. S. (2024). Analyzing Factors Influencing Milk Marketing Channel Strategies in Punjab. Indian Journal of Extension Education, 60(2), 105–108. https://doi.org/10.48165/ IJEE.2024.602RN3
Lyngkhoi, D. R., Singh, S. B., Singh, R. M., &Tyngkan, H. (2022). Trend analysis of milk production in India. Journal of Dairying, Foods & Home Sciences. https://doi.org/10.18805/ajdfr.dr-1789
Makridakis, S., Wheelwright, S. C., & Hyndman, R. J. (2018). Forecasting: Methods and Applications (3rd ed.). Wiley. Phebe, M. O., Chakravarty, R., & Joseph, M. B. (2024). Impact of Climate Change on Crop and Dairy Farming in Telangana: Agricultural Scientists Perspective. Indian Journal of Extension Education, 60(1), 41–45. https://doi.org/10.48165/IJEE.2024. 60108
Sharma, R., Chaudhary, J., Kumar, S., Rewar, R., & Kumar, S. (2022). Forecasting of milk production of crossbred dairy cattle by autoregressive integrated moving average (ARIMA) model. Indian Journal of Dairy Science. https://doi.org/10.33785/ ijds.2022.v75i04.011
Shraddha, K., & Kumar, S. (2024). Socioeconomic impacts of rural dairy farming. Journal of Agricultural and Rural Development.
18 INDIAN JOURNAL OF EXTENSION EDUCATION
Shumway, R. H., & Stoffer, D. S. (2017). Time Series Analysis and Its Applications: With R Examples (4th ed.). Springer. Singh, V., Gupta J., & Nain, M. S. (2014). Communication behaviour of dairy farmers: a source for milk quality improvement. Indian Journal of Extension Education, 50(3&4), 78-84.
Singh, V., Gupta, J., & Nain, M. S. (2016). Role and status of antecedent characteristics of dairy farmers in quality milk production. Indian Journal of Extension Education, 52(3&4), 171-176.
Sowmya, G., Shankar, V., & Thangavel, P. (2023). A novel finite
time complex-valued zeoring neural network for solving time varying complex-valued Sylvester equation. Journal of the Franklin Institute, 360(2), 1344–1377.
Suganthi, S. (2023). Prospects and performance of milk production in India: An analysis. International Journal for Multidisciplinary Research. https://doi.org/10.36948/ijfmr.2023.v05i04.3440
TAAS. (2023). National Dialogue on Sustainable Growth and Development of Indian Dairy Sector Proceedings and Recommendations. https://www.taas.in/Upload/Programs/638520 338278252384.pdf
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
