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Background: Buffalo milk production in India plays a significant role in the global dairy market, with a rich history deeply intertwined with the country’s economy and culture. Over six decades, the dynamics of buffalo farming have been pivotal in shaping India’s dairy landscape. Methods: This paper delves into the subject by analysing a comprehensive time series dataset spanning six decades. The focus lies on understanding the economic and cultural significance of buffalo farming, particularly in relation to milk production. Four forecasting models-ARIMA, SES, Seasonal Naive and ETS-are employed to discern temporal patterns in buffalo milk production. Result: The study reveals that the ARIMA and ETS models outperform SES and Seasonal Naive models in capturing and elucidating data behaviour. Their superior performance underscores their efficacy in predicting buffalo milk production trends accurately. These findings offer valuable insights for policymakers and stakeholders aiming to optimize buffalo milk production and foster long-term growth in India’s dairy sector.
Venu et al. (Tue,) studied this question.