Artificial insemination for milk production in India: A statistical insight

AMIT SAHA; SANGEETA BHATTACHARYYA

doi:10.56093/ijans.v90i8.109314

Authors

AMIT SAHA Central Silk Board, Ministry of Textiles, Government of India
SANGEETA BHATTACHARYYA ICAR-Central Citrus Research Institute, Nagpur, Maharashtra

https://doi.org/10.56093/ijans.v90i8.109314

Keywords:

Artificial insemination, Linear regression, Milk production, Support vector regression

Abstract

Though India is a global leader in milk production, on the flip side, about 80% cattle belonging to indigenous and non-descript breeds are low yielders whose productivity needs to be improved by adopting appropriate breeding techniques and Artificial Insemination (AI) comes to this rescue. AI plays a vital role in improving the productivity of bovines by upgrading their genetic potential thereby enhancing the milk production and productivity in the country. Though milk production is influenced by a number of factors, the authors analyzed one of the revolutionary innovations in Indian dairy sector, the artificial insemination (AI) in bovines which was introduced in India in 1951-56. Hence a statistical approach to inspect the influence of artificial insemination as a factor behind the growth in milk production in India was undertaken. In this study, Linear Regression (LR) and Support Vector Regression (SVR) were utilized. LR was used to establish the linear relationship between variables and determine the role of AI in that relation. SVR is an eminent machine learning technique which works on the structural risk minimization principle to minimize the generalization error which leads to better prediction accuracy, whereas LR provides the value by which one can estimate that to what extent AI can show it's impact on milk yield. Empirical results noticeably reveal the positive impact of AI on milk yield using LR and better prediction accuracy of SVR as compared to LR for both in training and testing dataset.

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