| Summary: | The modeling of lactation curves is an essential aspect of formulating farm managerial practices in dairy cows. In this study, we propose and examine a Markov-Dependent stochastic approach to modeling lactation curves in dairy cows, with the aim of developing a model that accurately fits lactation curves for a maximum number of lactations. Specifically, we develop a special type of Gamma Type Markov Chain Model that considers the first-order linear regressive property, which makes the model more realistic and reliable. We compared the proposed model with three other models - quadratic model, mixed log function, and wood model - using various goodness of fit measures such as adjusted R2, root mean square error (RMSE), and Bayesian Information Criteria (BIC). Our results showed that lactation curve modeling using the proposed model could help set management strategies at the farm level. However, it is important to optimize the modeling process regularly before implementing these strategies to enhance productivity in dairy cows. Our study contributes to the existing literature by proposing a novel approach that accounts for Markov dependence and linear regression in modeling lactation curves, which can lead to more accurate and reliable predictions. This modeling approach has practical implications for dairy farmers who seek to maximize productivity and efficiency while minimizing costs.
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